Cost reactive scheduler and method

ABSTRACT

A scheduling system and method for moving plural objects through a multipath system described as a freight railway scheduling system. The scheduling system utilizes a cost reactive resource scheduler to minimize resource exception while at the same time minimizing the global costs associated with the solution. The achievable movement plan can be used to assist in the control of, or to automatically control, the movement of trains through the system.

This application is a continuation-in-part application of applicationSer. No. 09/129,863 filed Aug. 6, 1998, now U.S. Pat. No. 6,154,735,which is a divisional application of Ser. No. 08/787,168 filed Jan. 23,1997, now U.S. Pat. No. 5,794,172, which is a divisional application ofSer. No. 08/299,271 filed Sep. 1, 1994, now U.S. Pat. No. 5,623,413.

BACKGROUND OF THE INVENTION

The present invention relates to the scheduling of movement of pluralunits through a complex movement defining system, and in the embodimentdisclosed, to the scheduling of the movement of freight trains over arailroad system.

Today's freight railroads consist of three primary components (1) a railinfrastructure, including track, switches, a communications system and acontrol system; (2) rolling stock, including locomotives and cars; and,(3) personnel (or crew) that operate and maintain the railway.Generally, each of these components are employed by the use of a highlevel schedule which assigns people, locomotives, and cars to thevarious sections of track and allows them to move over that track in amanner that avoids collisions and permits the railway system to delivergoods to various destinations. A basic limitation of the present systemis the lack of actual control over the movement of the trains.

Generally, the trains in presently operating systems are indirectlycontrolled in a gross sense using the services of a dispatcher who setssignals at periodic intervals on the track, but the actual control ofthe train is left to the engineer operating the train. Becausecompliance with the schedule is, in large part, the prerogative of theengineers, it is difficult to maintain a very precise schedule. As oneresult, it is presently estimated that the average utilization oflocomotives in the United States is less than 50%. If a betterutilization of these capital assets can be attained, the overall costeffectiveness of the rail system will accordingly increases.

Another reason that the train schedules have not heretofore been veryprecise is that it has been difficult to account for all the factorsthat affect the movement of the train when attempting to set up aschedule. These difficulties include the complexities of including inthe schedule the determination the effects of physical limits of powerand mass, the speed limits, the limits due to the signaling system, andthe limits due to safe train handling practices (which include thosepractices associated with applying power and breaking in such a manneras to avoid instability of the train structure and hence derailments).

There are two significant advantages that would be associated withhaving precise scheduling: (1) precise scheduling would allow a betterutilization of the resources and associated increase in total throughput(the trains being optimally spaced and optimally merged together to forman almost continuous flow of traffic), and, (2) to predict within verysmall limits the arrival times of trains at their destination.

This arrival time in the railroad industry is often referred to as“service reliability” and has, itself, a two fold impact: (1) itprovides the customer with assurance as to precisely when his cargo isgoing to reach its destination; and (2) for intermediate points alongthe movement of the trains it allows the planning of those terminusresources to be much more efficient.

For example, if the terminus of a given run is an interchange yard, andthe yardmaster has prior knowledge of the order and timing of thearrivals of a train, he can set up the yard to accept those trains andmake sure that the appropriate sidings are available to hold thosetrains and those sections of cars (or blocks of cars) in an favorablemanner. In contrast, unscheduled or loosely scheduled systems result intrains arriving at an interchange yard in somewhat random order, whichprevents the yardmaster from setting up the actual sidings, runs andequipment which will be required to optimally switch the cars to bepicked up for the next run beyond that interchange yard.

Similarly, if the terminus is a port where there is unloading equipmentinvolved, and removing the cargo from the train and transferring it toships requires a set of resources that must be planned for the cargo,the knowledge of the arrival time and the order and sequence of arrivalbecomes extremely important in achieving an efficient use of terminalequipment and facilities.

For a complete understanding of the present invention, it is helpful tounderstand some of the factors which inhibit the efficiency of prior arttransportation systems, particularly railway systems. Presently, trainsoperate between many terminal points generally carrying the goods ofothers from one terminal to another. Trains may also be hauling emptycars back to a terminal for reloading and may be carrying equipment orpersonnel to perform maintenance along the railway. Often, freightrailways share the track with passenger railways.

Freight service in present railways often has regularly scheduled trainsoperating between various terminals. However, the make up of the trainsvaries widely from one trip to another. Further, the length, mass, andoperating characteristics of the freight trains will vary substantiallyas customers' requirements for carriage among the various terminals andthe equipment utilized often vary substantially. Freight trains may alsobe operated on an ad hoc basis to satisfy the varying requirements ofthe train's customers for carriage. Accordingly, from day to day, thereare a substantial changes in the schedule and make up of freight trainsoperating on a particular railway system.

To meet the substantially varying needs for freight rail carriage,railway systems generally have a fixed number of resources. For example,any particular railway system generally has a signal network of track, afinite number of locomotives, a finite number of crews, and othersimilar limitations in the railway systems which can be used to meet thevarying customer requirements.

The difficulties in meeting the customers' requirements of a freightrailway system are often exacerbated by the fact that many railwaysystems have long sections of track bed on which only one main track islaid. Because the railway system generally has to operate trains in bothdirections along such single track sections, the railway system mustattempt to avoid scheduling two trains so that they occupy the sametrack at the same time, and must put into place systems and proceduresto identify such collision possibilities and to take some action toavoid them.

Similarly, when trains are running along a single track, a relativelyfast train may approach from behind a relatively slower train travellingin the same direction. Generally, the railway system must both attemptto schedule such trains in a way that the faster train will be permittedto pass the slower train and to identify during the operation of thetrains any situation in which one train is approaching a collision tothe rear of another train.

Situations in which two trains meet head on or in a passing situationare often handled by the railway system by the use of relatively shorttrack segments or “sidings” on which one or more trains may be divertedoff of the main track while another train passes. After the train issafely passed on the main track, the diverted train may then bepermitted to return on its journey on the main track. In the railwayindustry, such situations are called “meet and pass” situations.Obviously, meet and pass operations can significantly offset the abilityof any train to meet a particular schedule.

With reference to FIG. 1, a general system for managing meet and passsituations may include a main track 10, a side track 20 which isselectively utilized through switches 22. The switches may be manuallyoperated or may be remotely operated through a central control point foreach segment of track known generally as a HUT 24. The HUT 24 mayreceive signals from track sensors 26 which indicate the presence of atrain on a section of track. The train system may also include aspects28 which are illuminated lamp systems indicating to the engineer on agiven train whether or not the segments of rail immediately in front ofthe train and the next segment beyond are clear of traffic. Typically,in present railway systems, the operation of the aspects 28 iscontrolled primarily by track sensors 26 and a suitable electroniccontrol logic in the HUT 24.

Generally, train detection sensors 26 operate along a length of trackwhich may be as short as a half mile and may be in excess of two miles.Longitudinally adjacent sections of track are isolated into separatesegments by discontinuing the track for a brief length, on the order ofone quarter inch, and, optionally, placing an electrical insulator inthe gap between the segments. In this way each segment of track iselectrically isolated from longitudinally adjacent segments.

A voltage differential is applied between the two rails of a track andwhen a train is present, the metal wheels and axle of the trains serveas a conductor electrically connecting, or shorting, one of the rails ofthe track to the other rail, an electrical condition which can be sensedby the track sensor 26 and indicated to the HUT 24.

In present systems, the track sensors 26 between control points such asswitches, are often OR'd together in the signal provided to the HUT 24.Thus, the HUT 24 is able to determine if a block of track betweencontrol points is occupied, but may not be able to determine whichsegment(s) within that block of track holds the train.

The HUT 24 may send information regarding various of the conditionssupplied to it from the various sensors to a central dispatch facility30 by the way of a code line 32. The present systems, as describedabove, provides positive separation between trains so long as theengineer obeys the light signals of the aspects 28.

One difficultly known in present railway systems such as that shown inFIG. 1 is the lack of precise information as to the location of trainsalong the track. In a meet and pass situation, one of the trainsinvolved must be switched, for example, to the side track 20. Thisswitching on to the side track 20 must be accomplished well enough inadvance so that the train being switched to the side track is on theside track a sufficiently large length of time to permit a safety marginbefore the passage of the other train. The safety margin is necessarilyrelated to the precision with which the location of both of the meetingtrains is known. For example, if it is known that a train travellingthirty miles an hour is located somewhere in a block of track of twentymiles in length, it may be necessary to place an oncoming train onto asiding for at least two-thirds of an hour to await the passage of theother train.

To improve this situation a prior art system, called the Advanced TrainControl System (ATCS) has been designed and includes transponders,locomotive interrogators, and radio communications. In the ATCS system,transponders are placed between or near the rails of the tracks atvarious points along the track both between control points such asswitches and outside of the control points. Interrogators inside alocomotive activate a transponder by emitting a signal which is detectedby the transponder. Each transponder contains a unique identificationwhich is transmitted back to the locomotive while the locomotive and thetransponder are in close proximity. The identification information maythen be sent to a computer on board the locomotive and retransmitted viaa communication system 34 to the central dispatch 30. Between thepassage over sequential transponders, the computer on board thelocomotive can use signals from its odometer to compute the locomotive'sapproximate location.

Note that in such a system, the odometer error provides an uncertaintyas to the train's position along the track which increases as the trainmoves from one transponder to another and which is essentially zeroedwhen the train passes over the next transponder. By placing transponderssufficiently close together, the accuracy of the position information ofthe train may be kept within limits. Of course, the placement oftransponders along the entire railway system may substantially increasemaintenance costs as the transponders are relatively sensitiveelectronic elements in a harsh environment. In addition, if onetransponder is out, the odometer error will continue to build providingadditional uncertainty as to the knowledge of the position of the train.

The results of the meet and pass system in a railway system (a) in whichthe train's position within the system is not exactly known and (b) inwhich the engineers are running largely at their own discretion can beshown diagrammatically by “stringlines” that are commonly used bypresent railway systems to schedule and review the efficiency ofschedules which have been executed.

With reference to FIG. 2, a stringline plots time along one axis andtrack miles or terminals along the other axis. The grid of FIG. 2, forexample, runs from 5:00 a.m. on a first day until 11:00 a.m. on thefollowing day and depicts movement along a track interconnecting Alphaand Rome with fifteen other control points in between. Within the gridformed by the time and miles, the movements of trains are plotted. Astrains move in one direction, for example from Rome toward Alpha, thestringline for a train appears as a right diagonal.

Trains starting their travel in the opposite direction, i.e. from Alphato Rome, appear on the stringline as a left diagonal. Where one trainmust be sided to await the passage of another, the stringline becomeshorizontal as time passes by without movement of the sided train. Forexample, train 11 was sided at Brovo for nearly two hours awaiting thepassage of the train 99 and train B2. Similarly train 88 was sidedtwice, once in Bravo to wait the passage of train F6 and a second timein Echo to await the passage of train G7.

As can be seen in the stringline chart of FIG. 2, a train can spend asubstantial amount of time in sidings (train 88, for example, spentalmost two hours of a five hour trip sitting at sidings).

If the position of the train along the track can be determined with anincreased degree of precision, the need for trains to sit in sidings fora long period of time awaiting a meeting train may be reducedsubstantially. Note, for example, with reference to FIG. 2, the train 88sat in the siding at Echo in excess of one hour prior to the passage oftrain G7. With more precise knowledge regarding the location of thetrains, train G7 may have been able to continue to run on the trackuntil the Hotel siding at which point it could be briefly sided to awaitthe passage of train G7. Such a reduction in time spent in sidings wouldequate to a reduction in overall length of time needed to take anyparticular trip thus permitting greater throughput for the railwaysystem and reducing such costs as engine idling, crews, and other timedependent factors.

In the present day railway system, there is often little active controlover the progress of the train as it makes its way between terminals.Often, an engineer is given an authority merely to travel to a nextcontrol point, and the engineer uses his discretion, experience, andother subjective factors to move the train to the end of his authority.Often, the overall schedule utilized with such trains does not take intoaccount the fact that the train may be sided for a period of time, i.e.,the meet and passing was not put into the overall schedule.

Without explicitly planning for meets and passes, prior art trainsystems generally managed meet and pass situations on an ad hoc basis,as they arose, using the skill of the dispatcher to identify a potentialmeet and pass situation, make a judgement as to what siding should beused to allow the trains to pass, and to set the appropriate switchesand signals to effect his analysis. Because, as explained above, thedispatcher had train position data which was not particularly precise,the dispatcher may conservatively and prematurely place a train in asiding, waiting an unnecessarily long period of time for the passage ofthe other train.

Moreover, the dispatcher generally controls only a portion of the railsystem and his decision as to which train to put into a siding and whichsiding to use may be correct for the single meeting being handled.However, this “correct” decision may cause severe problems as thenow-delayed train meets other trains during its subsequent operationunder the control of other dispatchers.

In general, the entire railway system in the prior art was underutilizedbecause of the uncertainties in the knowledge of the position of thetrains along the track and because of the considerable discretion givento train engineers who determine the rates at which their trainsprogressed along the tracks. No matter how well a particular system oftrains is scheduled, the schedule cannot be carried out in presentsystems because of the variability in performance of the various trains.

Scheduling systems in the prior art generally attempted to scheduletrains in accordance with the manner in which the train system wasoperated. Thus, with some exceptions, the schedule was determined onlyon a “gross” data basis and did not take into account the specificcharacteristics of the trains which were being scheduled nor the finedetails of the peculiarities of the track over which they were beingscheduled.

Because system schedulers were generally used only to provide a“ballpark” schedule by which the train dispatcher would be guided, priorart scheduling systems did not generally identify conflicting uses oftrack, leaving such conflicts to be resolved by the regional dispatcherduring the operation of the trains.

Desirably, a schedule should involve all elements or resources that arenecessary to allow the train to move, these resources ranging from theassignment of personnel, locomotives and cars, to the determination ofroutes, the determination of which sidings will be used for whichtrains, as well as the precise merging of trains such that withappropriate pacing, the main lines can be used at capacity.

In the prior art, however, a number of difficulties have been associatedwith these types of schedules. These difficulties fell into severalcategories: (1) the immense computational requirements to schedule allthese resources very precisely; (2) the inability to predict the actualdynamics of the train and its motion that would be required to safelyhandle a train over a given piece of track; and (3) a precise schedulewas practically impossible to implement because there were no commandsavailable to the crew on the train or directly to the locomotivesubsystem that would cause it to follow any precise schedule that hadbeen established. The movement of the train in present systems isgenerally within the prerogative of the engineer driving the train,within of course the limitations of the signalling system in partcontrolled by the dispatcher and in part by the occupancy of the trackby other trains.

Previous attempts at performing a system wide optimization functionwhich precipitated a very detailed schedule. Such attempts have not beensuccessful due in part to the prohibitively large computationalrequirements for performing an analysis of the many variables. In fact,when the dimensions of the problem are taken into account, the number ofpermutations of solutions that are possible can represent an extremelylarge number. Consequently, exhaustive search algorithms to locate abest solution are impractical, and statistical search algorithms havenot generally been effective in problems of this scope.

OVERVIEW OF THE PRESENT INVENTION

A first step in providing a precision control system is the use of anoptimizing scheduler that will schedule all aspects of the rail system,taking into account the laws of physics, the policies of the railroad,the work rules of the personnel, the actual contractual terms of thecontracts to the various customers and any boundary conditions orconstraints which govern the possible solution or schedule. Theseboundary conditions can include things such as extrinsic traffic, (whichin the U.S. is most often passenger traffic) hours of operation of someof the facilities, track maintenance, work rules, etc.

The combination of all these boundary conditions together with a figureof merit, if operated on by an appropriate optimizing scheduler, willresult in a schedule which maximizes some figure of merit. The figure ofmerit most commonly used is the overall system cost in which case themost optimum solution is the minimum cost solution.

Since the constraints of such a system are variable, (i.e. likely tochange from day to day) the present invention may be structured tofacilitate the use of new boundary conditions or constraints, or newcontractual terms. For example, if a contract has just been signed whichinvolves a penalty clause of a certain magnitude for late delivery, thenan optimizing scheduler should take that penalty into account and allowit to be incurred only when that becomes the lesser cost option of thevarious scheduling options available.

Upon determining a schedule, the present invention determines a movementplan which will carry out the schedule in a realizable and efficientmanner. As a next step, the present invention incorporates into theschedule the very fine grain structure necessary to actually to controlthe movement of the train. Such fine grain structure may includeassignment of personnel by name as well as the assignment of specificlocomotives by number and may include the determination of the precisetime or distance over time movement of the trains across the railnetwork. This precise movement of the trains may include all the detailsof train handling, power levels, curves, grades, wind and weatherconditions such that the train is able to actually follow in detail themovement plan.

Finally, the present invention provides the movement plan to the personsor apparatus which will utilize the movement plan to operate andmaintain the train system. In one embodiment, the movement plan can beprovided merely to the dispatching personnel as a guide to their manualdispatching of trains and controlling of track forces. In anotherembodiment, the movement plan may be provided to the locomotives so thatit can be implemented by the engineer or automatically by switchableactuation on the locomotive.

While there is particular utility in freight railway systems, it shouldbe noted that the system and method of the present invention haveapplicability beyond a railway network. The disclosed system and methodmay be viewed as a transportation system in which in general thevariable are being solved simultaneously as opposed to being solvedsequentially. It is only with such a simultaneous solution that it ispossible to achieve near optimality.

Another factor that influences the overall efficiency of the railsystem, particularly the capacity of the given rail system, is theminimum spacing of the trains and the relative speed of the trains. Inthe prior art, the concept of the moving block operation has beenproposed, with a moving block consisting of a guard band or forbiddenzone that includes the train and a distance in front of every train thatis roughly associated with the stopping distance for that train. Thisconcept eliminates the fixed spacing that is associated with the currentfixed block signalling systems.

However, the complexity of a moving block has been difficult to realizedue to the fact that the stopping distance of a train is a function ofmany factors, including the mass of the train, the velocity of thetrain, the grade, the braking characteristics of the train and theenvironmental conditions. One benefit of the ability to perform planningwhich includes detailed evaluations and analysis of the dynamics of themovement of the train, is that the stopping distance of a specific trainis a natural by-product. The use of this precision train control allowsthe computation of the moving block guard band and permits trains to bespaced as close as their stopping distances will allow. The net resultsis a significant increase in the total throughput capability of a givenrail corridor.

The train movement planning system disclosed herein is hierarchial innature in which the problem is abstracted to a relatively high level forthe initial optimization process, and then the resulting course solutionis mapped to a less abstract lower level for further optimization. Thishierarchial process means that the solution space over which the searchis occurring is always diminishing as additional detail is incorporatedin the search for a solution. Furthermore, statistical processing isused at all of these levels to minimize the total computational load,making the overall process computationally feasible to implement.

An expert system has been used as a manager over these processes, andthe expert system is also the tool by which various boundary conditionsand constraints for the solution set are established. As an example, themovement of a passenger train through the network at a predeterminedtime may be set as one of the boundary conditions on the solution space,and other trains are moved in the optimum manner around that constraint.As another example, the scheduling of work to be performed on aparticular section of the track at a particular time may be set as aboundary condition and trains may be moved around that constraint in anoptimum manner.

The use of an expert system in this capacity permits the user to supplythe rules to be placed in the solution process. Consequently, everychange from work rule changes to contractual changes can be incorporatedby simply writing or changing a set of rules.

In some cases it can be desirable to allow the optimization process toschedule activities which normally are precluded by fixed constraints.For example, the railway maintenance activity could be considered aprescheduled constraint around which the train schedule should be moved.On the other hand, the constraint that is put into the rule base may bethat so many hours of maintenance activity on a given section of trackmust be performed and that the cost per hour of that operation is moreat night than in the day. Under those conditions, the scheduler may beallowed to schedule that activity in concert with scheduling themovement of the trains such that the overall cost of operation isminimized.

A very important aspect with the use of precision scheduling is theability to handle exceptions when they occur. The most common problemwith fixed schedules that are set up far in advance is that anomaliesoccur which cause elements of the network to get off schedule, and thoseoff-scheduled elements will ripple through the system causing otherelements to get off-schedule. For example, the late arrival of a trainon one trip may cause a locomotive to be unavailable for a plannedsecond trip, and the lateness of the second trip will cause again thelocomotive to not be available for a third trip. Thus ripple effects arecommon.

A key element of the globally scheduled system with fine grain controlas provided by the present invention is that it has continuousmonitoring of anomalies as they occur, and allows rescheduling tocompensate for the presence of these anomalies. This exception handlingcapability begins with the anomaly being reported to an exceptionhandling logic element which determines at what level the anomaly may beresolved. For example, a given train which has deviated from its plan inexcess of a predetermined tolerance could be an anomaly that could becorrected simply by small changes to the adjacent trains. On the otherhand, an anomaly of a larger magnitude such as a derailment which fouleda given track would cause a large scale rescheduling including use ofalternate routes. Such large scale rescheduling would be moved up to aglobal or system wide planning level which would permit a reoptimizationof the plan around that major anomaly.

There is a temporal aspect to this rescheduling activity in that theanomaly being reported must be acted on immediately for safety reasons,and then it must be acted on for short term optimization, and then itmay be acted on for global rescheduling. Thus, the anomaly resolution orexception handling process can be involved in various levels of ahierarchial planning system in time sequence until the anomaly is fullyresolved.

In the existing situation, the most common effect of an anomaly inpresent systems is to negate large portions of a predetermined schedule.In general in the freight railroad business, major perturbations to theschedule are not recovered for at least 24 hours. Unfortunately,anomalies happen with great frequency, some of them as small as loss ofone locomotive in a three locomotive consist, which causes that train tohave two thirds the power for which it had been scheduled. Or anomaliesare simply that the engineer has not attempted, or been unable, to stayon schedule. Without regard to the cause, they occur with greatfrequency and as a result most freight railroads do not maintain anysort of close coupling with predetermined schedules. The performanceagainst schedules is often so bad that crew changes are required toprevent unscheduled stops due to crews exceeding maximum allowed worktime.

In the optimization process it is important to understand the totalscope of what is necessary to actually achieve the minimum operatingcost. Very often optimization plans are based on the concept of prioritywhere certain elements of the operation (certain trains or certain typesof shipments) are given a higher priority than others because of thefact that they are considered to be more time critical.

In a true optimization technique the notion of priority per se should beimplicit but not explicit. The reason is that a given train, although ofhigh priority in the sense that it must meet a deadline (or the impactof missing a deadline is significant), may not generate any additionalrevenue if it is early. To say it another way, being early may not be anadvantage, but being late may cause a considerable negative impact. In atrue cost optimization plan, priority must be tempered and the priorityfunction must be delayed within the “don't be late” constraint.

One of the fundamental principals in optimization is that each elementof the operation have associated with it some incremental cost in thecriteria being optimized. Incremental cost can be fuel cost, hourly costof personnel, hourly use cost of locomotives or hourly use cost timesdistance travelled of locomotives. The actual incremental cost factorshould go into the optimization plan, including penalties.

The plan must include nonlinearities in the incremental costs to allowfor the fact that at certain points in the delivery time schedule theactual cost will either go up as a step function or as a slope. As anexample where there is no advantage associated with an early delivery,the failure to deliver a given cargo might be a $1000 fixed penalty ifnot delivered on time and an additional $1000 per hour demurrage chargeif it causes a ship to stay in port.

A true optimization plan is one whereby the variables including theassignment of resources are juggled such that the overall cost isminimized. An example would be two trains that were moving down a tracktowards a destination, one of which was four hours late and one of whichwas one half-hour late, with both trains having a significant but fixedpenalty for being late. The logical solution would be to refrain fromdoing anything for the four hour late train because of the impossibilityof ever meeting its schedule, and to give the half-hour late train everyopportunity to recover the half-hour and avoid the penalty of beinglate. In such a scenario, the four late train may be given a much lowerpriority than a bulk commodity train since the bulk commodity train mayinvolve more resources being used.

In the present invention, it is the global or the overall optimizationfor cost which controls rather than predetermined priorities, withpriorities used only as cost factors. The total cost includes theoperating costs such as fuel and rolling stock utilization as well asthe delivery costs caused by contractual terms and commitments. Onlywhen all of these cost factors are taken into account is it possible tocome up with a true minimum cost plan. In the known prior systems, nosuch plan is possible because no technique is available which actuallycomputes the incremental cost associated with each of the decisions. Asa result, suboptimal plans are often generated based on the intuition ofdispatchers and planners.

Partial Listing of Objects.

Accordingly, it is an object of the present invention to obviate theabove deficiencies of known systems and to provide a novel system andmethod for scheduling the movement of a number of objects through amultipath delivery system.

It is another object of the present invention to provide a novel systemand method for optimizing the movement of a number of objects through amultipath delivery system.

It is still another object of the present invention to provide a novelsystem and method in which a detailed movement plan is bound to thecontrol of a delivery system.

It is still a further object of the present invention to provide a novelsystem and method to operate a delivery system according to a schedulesuch that the deviation from the schedule at any moment in time isminimized.

It is another object of the present invention to provide a novel systemand method to manage the movement of carriers in a delivery system suchthat local conflicts are resolved with reference to the effects of suchresolution on the entire system.

It is a further object of the present invention to provide a novelsystem and method in which conflicts in the use of system resources arereduced by managing the extent of the periods of such conflict.

It is yet a further object of the present invention to provide a novelsystem and method in which conflicts in the use of resources are reducedby closely scheduling and operating the use of such resources.

It is still another object of the present invention to provide a novelsystem and method in which the delays in a delivery system are reducedby providing a detailed and realizable plan of movement and providing ameans for carrying out the detailed and realizable plan.

It is yet another object of the present invention to provide a novelsystem and method for providing a plan for the movement of a number ofobjects through a multipath delivery system which is physicallyattainable by the objects being moved, and which as a result can be usedto control the movement of those objects.

It is yet still another object of the present invention to provide anovel system and method for providing a plan for the movement of anumber of objects through a multipath delivery system in which theobjects being moved are converted to time intervals for processing.

In another aspect, the present invention provides a novel method andapparatus for optimization which utilizes different levels ofabstraction in the course scheduling and fine planning stages.

It is another object of the present invention to provide a novel systemand method for optimizing where the amount of detail in the movementbeing optimized in inversely related to the solution space.

It is yet another object of the present invention to provide a novelsystem and method for optimizing using a rule based inference engine toprovide constraints for a constraint based inference engine.

It is yet still another object of the present invention to provide anovel system and method for optimizing using the combination of rulebased and constraint based inference engines in developing a movementplan, with further optimization using a procedure based inferenceengine.

It is yet a further object of the present invention to provide a novelsystem and method for optimizing with consideration of both operationaland delivery costs.

In another aspect, it is an object of the present invention to provide anovel model and method of modeling capable of different layers ofabstraction.

In another aspect, it is an object of the present invention to provide anovel computer and method of computing which combines simulatedannealing and branch and bound techniques in developing solutions tocomputational problems.

It is another object of the present invention to provide a novelcomputer and method of computing with intelligent focusing of simulatedannealing processes.

It is yet still another object of the present invention to provide anovel cost reactive resource scheduler to minimize resource exceptionwhile at the same time minimizing the global costs associated with thescheduling solution.

These and many other objects and advantages of the present inventionwill be readily apparent to one skilled in the art to which theinvention pertains from a perusal of the claims, the appended drawings,and the following detailed description of the preferred embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of the prior art systems.

FIG. 2 is a pictorial depiction of a prior art stringline used in thescheduling of an embodiment of a system of the present invention.

FIG. 3 is a functional block diagram of the system of the presentinvention.

FIG. 4 is a functional block diagram of the system wide planner or orderscheduler of FIG. 3.

FIG. 5 is system flow diagram of the implementation of the resourcescheduler of FIG. 4 in a COPES shell.

FIG. 6 is a functional block diagram of the movement planner portion ofthe planner/dispatcher of FIG. 3.

FIG. 7 is a functional block diagram of the physical model of FIG. 6.

FIG. 8 is a schematic illustration of system operation.

FIG. 9 is a pictorial illustration of the multilevel abstraction of thethree dimensional model of FIG. 6.

FIG. 10 is a functional block diagram of the train controller of FIG. 3as may be utilized in a locomotive.

FIG. 11 is a functional block diagram of a portion of the traincontroller of FIG. 10.

FIG. 12 is a graphical representation of the ideal trajectory of thetarget resource exception for the search phase of the cost reactivescheduler.

FIG. 13 is a graphical representation of the simplified trajectory ofthe target resource exception for the search phase of the cost reactivescheduler.

DETAILED DESCRIPTION OF A FREIGHT RAILWAY SCHEDULING SYSTEM

Many of the advantages of the present invention may be understood in thecontext of a freight railway scheduling system, and preferredembodiments of the various components of the invention and the operationthereof are described below in such context.

Overall System.

With reference to FIG. 3, a train scheduling and control system inaccordance with the present invention may include a system wide planneror order scheduler 200, a planner/dispatcher 204, a safety insurer 206and a train controller 208.

In overall terms, and as explained further below, the system wideplanner 200 is responsible for overall system planning in allocating thevarious resources of the system to meet the orders or demands on thesystem in an optimal manner. The system wide planner 200 develops acoarse schedule for the use of the various resources and passes thisschedule to the planner/dispatcher 204. The planner/dispatcher 204receives the coarse schedule from the system wide planner 200 and, asexplained further below, determines a detailed schedule of the resourcestermed a movement plan. The movement plan may then be used by thedispatching portion of the planner/dispatcher 204 to be transmittedultimately to the train controller 308 on board the locomotive in thetrains being controlled.

The movement plan developed by the planner/dispatcher 204 may be checkedby a safety insurer 206 to verify that the movements being commanded bythe planner/dispatcher will not result in any of the trains of thesystem being placed into an unsafe situation.

With continued reference to FIG. 3, the planner/dispatcher 204 may alsogenerate appropriate command signals for the various track elements 210(such as switches) to configure the railway system as needed to carryout the movement plan in an automated embodiment in a system of thepresent invention. As with the movement plan signals, the signals to thetrack elements 210 may be verified for safety by the safety insurer 206.

Information regarding the position of the train and the settings of thetrack elements may be sent back to the planner/dispatcher.

In the event that the planner/dispatcher 204 is unable to develop aschedule for all the required services in the schedule, or in the eventthat a train is unable to meet such schedule, exceptions are passed backup the communication chain for handling by the next higher level asneeded.

It may be noted that in each level of the system in FIG. 3, the systemtakes into account the effect of the size (mass) and power of the train,the various track parameters and train handling constraints on thescheduling and movement process. Track parameters include those physicalcharacteristics of a particular track which affect the speed at whichthe train may traverse the track and which affect the rate of change inspeed or power which occurs while a particular train is running alongthe track. These parameters include, for example, the grade of thetrack, its curvature and slope, and the condition of the track bed andrails. By generating a schedule which takes into account such trackparameters, the system wide planner 200 is able to generate a coarseschedule which has a high probability of being successfully implementedduring the detailed planning of the planner/dispatcher 204. Likewise,the use of such track parameters by the planner/dispatcher 304 willensure that the developed movement plan is realistic and can be followedsafely and closely by the actual train.

Similarly, all levels of the system may include train handlingconstraints within their determination of a coarse schedule, movementplan and the commands used to control the train. Train handlingconstraints include experiential and other factors by which it is knownand accepted that trains should be operated. These constraints includebraking techniques and switch crossing considerations to avoidderailment.

For example, a long train which has just come over the crest of a gradeis considered to be “stretched” because all of its intercar couplingsare in a stretched or tensioned position. As the front portion of thetrain begins to go down the grade on the opposite side of the crest, thecars on the downgrade tend to compress if the engine is slowed, and itmay be dangerous to apply dynamic brakes (i.e, the braking system whichoperates only at the engine). As the couplings between cars compress aseach car is slowed by the cars in front of them, the tendency for thetrain to buckle is a known cause of derailments. Such train handlingconstraints may vary by the size and type of train and are taken intoconsideration at each level of the system.

The planner/dispatcher 204 of FIG. 3 has two processes: aplanner/dispatching function and a movement planner. Theplanner/dispatching function is responsible for the movement of a trainfrom its dispatch (i.e., its earliest departure time) until its arrivalat its destination (port, mine, yard or terminal). The movement planner,as detailed below in connection with FIG. 4, takes the coarse scheduleinitially determined by the system wide planner or order scheduler 200and generates a detailed movement plan utilizing the details of thephysical attributes, the track parameters and train handlingconstraints.

The movement plan is a time history of the position of the trainsthroughout the plan and takes into account the physical forces which areexpected to occur during the actual carrying out of the plan. Forexample, the movement planner takes into account the inertia of thetrain and the track parameters, etc. to provide a movement plan in whichthe fact that the train does not instantly reach its desired speed isaccommodated.

Thus, the movement planner takes into account the speed changes and/ortime effects of the various constraints over the specific track uponwhich the trains are being planned. For example, if the movement plannerdetermines that a particular train will be placed on a siding, themovement planner accounts for the fact that the train may have to slowsomewhat for switching and, particularly if the train is stopped on thesiding, that the subsequent acceleration will not be instantaneous butwill be an increase in velocity over a finite period of time inaccordance with locomotive weight, track adhesion, weight of the train,grade and curvature. In this way, the movement planner generates theexact trajectory which the train is expected to follow.

This detailed movement plan should be contrasted with systems in theprior art in which plans are generated with respect, at best, to anaverage length of time which similar trains have required to traverse,or are expected to require to traverse, the same track segments. While,on average, the prior art averages of simulations may be fairlyaccurate, they typically assume characteristics which are not possibleto accomplish in the movement for actual train.

For example, the models of the prior art may model the travel betweentwo segments as an average speed over those two segments. If themovement plan is generated simply from the average speed, the movementplan will be inaccurate in anticipating the trajectory of the trainbecause the average speed in the model cannot instantly be obtained bythe actual train. When such an average speed is used in generating amovement plan, the train cannot actually implement such a plan, and suchplans cannot be used to control the trains.

In contrast with the prior art, the present invention takes into accountnot simply the average speeds between points but other factors whichaffect train speed and the time to various points between the segmentends. By so doing, the movement planner of the present inventionaccurately knows not only when a train will arrive in the end of aparticular segment but also where the train should be at any given timewhile in the middle of such a segment. Because the movement plannerknows the exact time that a train under its control will be at aparticular facility, such as a siding or an alternative track, it mayschedule meetings and passings more closely than in the prior art.

In the movement planner of FIG. 4, either fixed block or moving blockrules may be used. Fixed block rules reflect the segmentation of tracksinto fixed blocks or segments. Generally, in the prior art, the blocksize was set at the distance that the slowest stopping train would taketo stop. In train following situations, a following train would be keptbehind the leading train by at least a multiple of the length of thefixed block.

Typically, the headway between a following train and the leading trainwould be fixed at multiples of a fixed block size. Because the system ofthe present invention uses a very precise control geared specifically tothe capabilities and dynamics of the specific trains being handled, theseparation between trains can be made smaller than in the fixed blocksystems as they can be made to reflect the actual braking distance ofthe specific trains. Thus, the system of the present invention is notbased on a “worst case” braking scheme and the throughput of the railsystem is improved thereby.

With continued reference to FIG. 3, the movement plan generated by themovement planner of FIG. 4 is used by the planner/dispatcher 204 tocontrol the operation of the trains. In one embodiment, selectiveportions of the movement plan can be displayed to assist operatingpersonnel in dispatching trains and in correctly configuring the varioustrack elements (switches, signals, etc) as called for in the movementplan. In another embodiment of the present invention, the movement plancan be automatically dispatched by the planner/dispatcher 204 via thecommunications infrastructure to send the appropriate portions of themovement plan to the train controllers 208 aboard the locomotives and toremotely control the various track elements.

Both the movement plan signals and the track force controlling signalsmay be independently verified for safety by the safety insurer 206which, independently and without regard to schedule, confirms that theparticular movements being ordered and settings of track forces are safeand appropriate. The safety insurer may be any suitably programmedcomputer, particularly a computer with built-in hardware redundancy toeliminate the possibility of a single-point failure.

It is important to note the close tie between the movement plantrajectory as determined by the planner/dispatcher 204 and the trainmovement which is implemented by the train controller 208. If thetrajectory which was planned by the planner/dispatcher 204 was notsufficiently detailed, including factors such as inertia trackparameters and train handling, the train controller 208 would not beable to implement the plan and could be expected to inundate theplanner/dispatcher with exception notices.

Order Scheduler

With reference now to the system-wide planner or order scheduler 200illustrated in FIG. 4, it may include an extent of planning determiner304, an activity identifier 310, a candidate resource determiner 314, atrain action effects calculator 318 and a time interval converter in therule based inference engine shown above the dashed line 340. The orderscheduler 200 may also include a constraint based inference enginecomprising an interval grouper 324 and a resource scheduler 330. Adisplay 334 and terminal for other output devices (not shown) may beprovided in a utilization section.

As shown in FIG. 4, a new order for rail service may be applied via aninput terminal 302 to an extent of planning determiner 304. The ordermay be any request for rail service and may include an originationpoint, an earliest pickup time at the origination point, the destinationpoint, the latest delivery time to the destination point (after whichpenalties are applied), a cost function which defines the penalty to bepaid for late delivery and/or an incentive award for earlier delivery,and any other information appropriate to the class of the order.

An order may take the form of a request to move a specifically loadedtrain from point A to point B, to provide a round trip service betweentwo points, to execute a series of round trips with unspecified trains,to schedule a maintenance period for a specific segment of track orother rail equipment, etc. Thus, an order to pick up coal from a mineand deliver it to a port may require one or more trips, with each triprequiring a train resource, a sequence of track resources, mine loadingresources and a port unloading resource. The sequence of track resourcesis, of course, dependent upon the selection of a route if alternativeroutes are available.

The extent of planning determiner 304 also receive on an input terminal306 the data as to the available resources. A resource may be any entitywhich may be scheduled and for example, may be a locomotive, a freightcar, an entire train, terminal equipment such as a loader or unloader,track segments and any fixed or moving block associated therewith, ortrack or train maintenance equipment.

The extent of planning determiner 304 may also receive any scheduleexceptions via an input terminal 308. A schedule exception may be anypreviously scheduled event which will not be satisfied within aspecified time interval deviation from the schedule and may requirereplanning in conformity with company policy.

The extent of planning determiner 304 may also receive any extrinsictraffic which is to be included in the plan. Extrinsic traffic is anytraffic which is not subject to scheduling by the movement planner,e.g., prescheduled traffic. By way of example, an extrinsic scheduleexemption for the typical railway freight system may be the inviolateschedule of a passenger train over the same railway track system.

The extent of planning determiner 304 may be any suitable conventionalapparatus, preferably appropriately programmed general purpose computeror a special purpose computer, with the capability of analyzing theavailable data to generate the orders as to which scheduling is to beaccomplished.

The extent of planning determiner 304 provides orders to an activityidentifier and sequencer 310 via terminal 312 and the activityidentifier and sequencer 310 provides an activity list to the candidateresource determiner 314.

An activity is an event which requires one or more resources to beassigned for a period of time. By way of example, an activity may be theloading of a train with a bulk commodity which requires the assignmentof a train, the assignment of loading equipment, or the assignment oftrack at and in the vicinity of the loading point, each for a period oftime depending upon the capacity of the train and the characteristics ofthe loading equipment.

The activity identifier and sequencer 310 in turn provides a list of theavailable resources from terminal 306 which have the capability ofperforming the identified activity in the necessary time sequence. Theactivity identifier and sequencer 310 may be any suitably programmedgeneral purpose or special purpose computer with access to the requisitedata.

The list of candidate resources from the activity identifier andsequencer 310 may be provided via the terminal 316 to both the trainaction effects calculator 318 and the time interval converter 320. Thetrain action effects calculator 318 also provides an input signal to atime interval converter 320 as described below. The train action effectscalculator 318 may be any suitable conventional appropriately programmedgeneral purpose or special purpose computer with the capability toderive from data as to the composition of the train the effects whichthe terrain over which the train travels has thereon. While not limitedthereto, the effects of terrain on the acceleration and deceleration onthe train are particularly important. The calculator is provided withthe data base from the physical model of FIGS. 7 and 8 via a terminal321.

The time interval converter 320 may likewise be suitable conventionalgeneral purpose or special purpose computer capable of converting eachof the candidate resources to a time interval which takes intoconsideration train action effects.

The output signal from the time interval converter 320 may be applied byway of a terminal 322 to the interval grouper 324. The interval grouper324 also receives via terminal 326 the orders from the extent ofplanning determiner 304. The output signal from the interval grouper isapplied as a group of time intervals by way of a terminal 328 to ascheduler 330.

The interval grouper 324 may be any suitable conventional general orspecial purpose computer capable of calculating the total timeassociated with the execution of each trip using the candidateresources.

The resource scheduler 330 which receives the interval groups alsoreceives by way of an input terminal 332 data as to the performancemeasure by which schedules are evaluated. In addition, the scheduler 330receives a signal from the extent of planning determiner 304 indicativeof the resources available for the scheduling process. The output signalfrom the schedule 330 is applied to any suitable conventional display334 and to any other utilization device (not shown) by way of terminal336. The output signal from the scheduler 330 is the schedule which isalso fed back to the extent of planning determiner 304 as discussedbelow.

The resource scheduler 330 may be any suitable conventional generalpurpose or special purpose computer capable of scheduling the passage ofthe various trains over the track system with a high degree ofoptimization. However, and as discussed infra in greater detail inconnecting with FIG. 5, the resource scheduler 330 is desirably onewhich uses the well known simulating annealing techniques to approximatethe optimum solution.

In operation, the extent of planning determiner 304 determines theextent of planning to be performed from new orders and/or scheduleexceptions. With new orders, the extent of planning determiner 304 usesa set of rules defined by standard operating procedures, company policy,etc. as well as the current schedule from the scheduler 330 and thecurrently scheduled train movements or maintenance actions to determinethose actions eligible to be scheduled. Any extrinsic traffic must alsobe considered in determining the extent to which planning is to beaccomplished.

By limiting the planning, confusion among personnel and the inherentinefficiencies caused by constant schedule changes as well as theinefficiency resulting from changes to on-going or imminent activitiesmay be avoided.

The orders from the extent of planning determiner 304 are received bythe activity identifier and sequencer 310 and are used to generate anactivity list. For each order, a list of activities required to satisfythe order is identified. The activity list includes the sequence oftrack segments (i.e., route) which must be traversed in filling theorder. Route selection may be based upon cost analysis, upon previouslydetermined company policy or standard operating procedures. The activitylist is, of course, ordered sequentially so that it constitutes asequential list of each activity to be performed in the fulfilling ofthe order to be scheduled.

The activity list is supplied to the candidate resource determiner 314.For each of the resources on the activity list, the possibility ofassigning such resource to the specified activity is analyzed and aselection of rolling stock resources is made, typically based uponlimitations of the rolling stock or upon company policy. For example, aparticular destination such as a port for coal hauling operations maynot be able to unload certain types of rolling stock. In the samemanner, a particular type of train with a specified locomotive power maynot be able to move over the grade associated with the selected routewithout overheating the engine or stalling.

This list of resources which are candidates for each of the activitieson the activity list may be provided to the train action effectscalculator 318 and the time interval converter 320 as candidateresources. Thus the candidate resource determiner 314 serves to limitthe potential assignment of rolling stock and/or other resources to theactivities which it has the capacity to perform.

The train action effects calculator 318 and the time interval converter320 together compute the time required to complete the activity for eachof the activities listed on the activity list it receives and for eachof the candidate resources. For the movement of a train (loaded orunloaded), over a sequence of track segments, this computation may beperformed by a commercially available train performance calculator suchas the AAR TEM model.

Loading and unloading tasks may be computed by dividing the capacity ofa train by a constant loading (unloading) rate of the equipment at theterminal. This rate may be variable, in which event the time computationmust take the nonlinear characteristics of the equipment intoconsideration. Additional time should be included in theloading/unloading process to allow for positioning the train at theloader/unloader equipment. The time computed for each of the activitieson the activity list is adjusted for train action effects for each ofthe alternative resource candidate, and the time interval information isprovided to the time interval converter 320.

The time interval converter 320 translates the sequence of activities onthe activity list to a sequence of time intervals. This is accomplishedby using the data from the train action effects calculator 318 for eachof the activities identified by the candidate resource identifier 314.In the event that alternative resources are available for accomplishingany activity, then all alternative time intervals are computed for eachof the activities. Certain types of activities, such as maintenanceactivities, are provided with an externally specified interval of timeto completion and thus do not require calculations. The time intervalconverter 320 passes a list of time intervals grouped by resource aswell as by time to the interval grouper 324.

The interval grouper 324 receives the list of grouped intervals from thetime interval converter 320. The interval grouper 324 also receives theorders from the extent of planning determiner 304 and groups the timeintervals necessary to fulfill the orders in the logical sequence. Fortrips, the interval grouper 324 provides the time intervals required toperform the entire trip, but indicates which of the time intervals maybe divided, if necessary, into smaller intervals by the presence ofgaps.

Gaps represent the time periods which may be allowed to pass between thecompletion of one time interval and the initiation of the next timeinterval in the group. A gap may be the existence of a siding or othercapacity for holding a train for an interval of time, e.g. to permit thepassage of a second train. Any time interval immediately followed by agap (e.g., one associated with the passage of a train over a section oftrack to a siding) may be said to be a “gap-able time interval”. Theinterval groups defined by this process are passed to the resourcescheduler 330 as interval groups.

The interval groups are passed to the resource scheduler 330 which alsoreceived from the extent of planning determiner 304 a list of theresources available to schedule. Performance measures related to theorders which are provided by the customer are also provided to permitcost evaluation of the schedule as described below. The resourcescheduler 330 thus conducts a search for a schedule which satisfies theresource availability constraints, satisfies the internal constraintsand minimizes the performance measures.

As earlier indicated, the search for an acceptable schedule may employvarious suitable conventional techniques, but the preferred technique isthat of simulated annealing discussed above. If no acceptable scheduleis available because of the length of the group time intervals, theinterval groups are returned to the interval grouper 324 for division atthe gaps into smaller groups. After division, they may be returned tothe resource scheduler 330 and the scheduling process repeated. Thisscheduling process continues with smaller and smaller time intervalsuntil the interval groups can no longer be divided, as there are nogap-able time intervals in any group of time intervals.

At any time that the resource scheduler 330 can provide a schedule whichmeets the restraints placed upon it, that schedule is passed to thedisplay unit 334 as well as any other selected utilization meansattached to the terminal 336. This schedule is also applied to theextent of planning determiner 304 as part of its data base where theyet-to-be-completed components of the schedule are treated as scheduleexemptions in the determination of further planning.

In the event that the resource scheduler 330 cannot provide a schedulewhich conforms to all of the constraints, the best available schedule isreported along with an indication that the schedule has unresolvedconflicts. Information as to the resources and activities involved inthe conflict are identified.

The display 334 conveniently displays the resulting schedule for userexamination. A popular display is a standard string-line diagram used bythe railroads such as illustrated in FIG. 2 above.

Note that the components in the portion of the order scheduler 200 ofFIG. 3 above the horizontal dashed line 340 in FIG. 4 are components ofa rule based system, i.e., a rule based inference engine which providesthe constraints applied to the resource scheduler 330 and intervalgrouper 324. It is one aspect of the invention that both rule based andconstraint-based systems are utilized for scheduling orders. By thiscombination of inference engines, unusual efficiency in calculating theschedule is obtained.

The resource scheduler 330 performs globally optimized scheduling oftrain resources using an abstraction of train movement and resources.Choosing an abstraction for resources which leads to a realizablesolution in near real-time is key to reducing the search space requiredby the movement planner in developing a detailed movement plan.

Preferably, the resource scheduler 330 is implemented in the HarrisCorporation developed COnstraint Propagation Expert System (COPES)Shell. This shell provides a virtual engine for developing distributedalgorithms which may be implemented on one machine, or distributed overany number of machines in a TCP/IP environment. This engine provides aconstraint propagation inferencing environment with built-incommunications capabilities and a unique discrete-simulation capability.It is well-known as described in the 1993 Goddard Conference on SpaceApplications of Artificial Intelligence”, page 59.

One of the advantages of developing the resource scheduler 330 in COPESis that it can receive asynchronous requests from the extent of planningdeterminer 304 and (a) stop the scheduling process, returning the bestsolution found to this point in time, or (b) abandon the currentscheduling process and start a new scheduling request based on recentsystem changes such as deviations from scheduled activities.

The resource scheduler 330 shown in FIG. 5 is a UNIX process whichschedules resources so as to fulfill a set of orders for rail service ina manner that satisfy a set of user-defined constraints. Multiple ordersmay be scheduled either in batch or sequentially. As earlier indicated,an order also has a time interval during which the service is to beprovided and a cost function which defines the penalty to be paid forlate delivery and/or the incentive award for early delivery. Once theresources are selected, the activity lists can be converted to asequence of time intervals by incorporating the train effects capturedin the resource usage data and these intervals can then be groupedtogether. These groups of time intervals can then be moved relative toone another using a novel search procedure referred to as FocusedSimulated Annealing to satisfy the constraints and obtain a lowest costsolution.

Focused simulated annealing is a distributed version of simulatedannealing written in COPES. It follows the traditional flavor ofsimulated annealing in the random generation of move operators with anenergy function which is to be minimized.

A. Generation of potential moves via constraints is random anddistributed.

B. Optimization allowed to take some bad moves in early stages.

C. As “temperature” is reduced less bad moves are allowed.

D. In final phases only good moves allowed.

Variables include starting temperature and the number of temperaturereductions steps. For each temperature reduction step it also includesthe number of reconfigurations, the number of successes and the numberof attempts.

What distinguishes this approach from traditional simulated annealing isits capability to focus its attention in an intelligent manner oncritical areas. In the early phase of search this focus is limited tocertain guiding information passed by the planner such as the likelihoodof the degree of constraint of the solution along with goals such asminimum siding usage, or earliest delivery, etc. This information isused by the focused simulated annealing technique to determine whetherto use certain move operators in the search process, and if so how oftento fire them relative to other operators. The generation of moveoperators is therefore more directed, although still random, than in theuse of traditional simulated annealing techniques.

Focused simulated annealing is distributed by allowing constraintroutines attached to each trip to make decisions themselves about howuseful modifying the current trip (e.g., start time, equipment assigned)would be to the overall situation. Each routine can schedule itsassociated trip for modification on a random basis with the time rangebeing a variable reflecting the importance of the next move of the trip(e.g. a larger time range indicating less importance).

The resource scheduler 330 employs a dynamic, distributed, robust, andefficient version of simulated annealing written in the COPES shell. Itis dynamic in that its behavior may be controlled by parameters passedwith scheduling requests by the system wide planner (such as demurragecosts in the form of a polynomial cost function), by parameters definedin the COPES database, and by information inherent in the schedulingproblem itself. It is a distributed algorithm in that train trips areCOPES class objects each having constraint objects bound to them whichfire independently of each other. The solution thus derived must be moreindependent of the problem domain than is the case with more sequentialalgorithms and is therefore a more robust approach. It is an efficientimplementation in that it employs a compact representation of eachresource required as COPES objects with availability profiles and atemporal logic approach which manipulates these availability profiles inan efficient manner as a trip is added or removed. The temporal logicalso considers constraints such as moving block distances. Global costsof such a move are modified as a side effect.

The operation of focused simulated annealing in COPES in the resourcescheduler 330 of FIG. 4 is illustrated in FIG. 5. With reference now toFIG. 5, a constraint-based system flow of a such a resource scheduler isillustrated. The bold names in ovals (such as op_resource_usage) are theconstraint routines (they are not limited to reducing the search spacebut may also generate solutions). They are only fired by the COPESinference engine when a class variable to which they are bound ismodified. The names shown in rectangular boxes (such as resource_usage)are class objects with state variables not shown in the interest ofclarity.

There are multiple instances of some class objects such as orders andtrips. Each trip instance, such as “tripO_state” is actually composed oftrip state variables, and trip_resource class objects defining thesequence of resources necessary to complete the trip. Each order iscomposed of enough trips to satisfy the order. Constraints are bound toeach trip and are the primary move operators to explore the searchspace.

The time interval converter requests a schedule from the resourcescheduler 330. The server_io constraint fires and moves this requestinto the interface state class which causes the op_resource_usageconstraint to fire. This constraint stores the pregenerated resourceusage times (from the time interval converter) for each train type usingeach resource. It also stores information about siding possibilitybetween two tract segments.

Requests for scheduling are now received via the op_capacity_requestmessage. This message contains information about the order as describedearlier, search goals, and constraints. The op_capacity_requestconstraint generates order class objects for each order, and enoughtrain trips to satisfy each order. It notifies the control_searchconstraint to begin Focused simulated annealing via the search_stateclass object.

Control_search initializes the search and annealing parameters and setsup for the first phase search. It activates all selected tripconstraints and randomly schedules them for firing. The schedule forfiring is a discrete-event queue reflected by scheduled modification ofclass variables in COPES. As each move operator is fired it checks tosee if one of the simulated annealing parameters indicates that a changeis required. If a change is required, the operator notifies thecontrol_temperature constraint which will lower the temperature andre-initialize search parameters for the next temperature.

At the end of the first phase, the control_search starts anotherannealing pass with half the number of attempts allowed at eachtemperature and with no higher energy steps allowed during this phase.Because the search is in a reasonable global optimal neighborhood, it isthen desirable to focus on better local solutions. Upon completion ofthe final phase, a directed search process is performed to furtherrefine the schedule and to compress the schedule if desired.

In the event that the resource scheduler cannot find a schedule whichsatisfies the constraints, it returns the best possible schedule alongwith an indication that an exception has occurred and the identity ofthe resources and activities involved in the exception.

The move operators performing the actual search are described below.Each one is an instance of the constraint routine bound to an instanceof a trip class. The behavior of the move operators is variabledepending upon the phase of the search, goals of the search, and theirlikelihood of improving the solution. At lower temperatures themove_trip and the mod_gap move operators reduce the start time rangethey will consider for the attached trip. This moves the emphasis fromglobal to local at lower temperatures. The change_equipment is onlyfired if it is determined at a low temperature that the train equipmentis over constrained in its current assignment. The move_group operatoris only fired at the end of phase one and if a tightly constrainedsituation is indicated.

At lower temperatures in the final phase, the move_trip and mod_gapoperators determine how likely they are to help the search by lookingfor over-utilization of availability profiles describing their resourceusage. If such over-utilization is detected, then the operators schedulethemselves to fire randomly but closer in time than would otherwise bethe case. The concept of energy is a weighted combination of resourceexceptions, operating costs, and goals such as earliest delivery. Theenergy function gives more emphasis to the most critical resources(e.g., mine, trains).

The following are the move operators used in the preferred system:

A. move trip—a constraint which moves a trip (which includes all tripresources and considers scheduling constraints, and costs). It moves thetrip back if the cost is no better. However, early in simulatedannealing the cost is allowed to be worse depending upon the temperatureand the oracle decision, avoiding local minimum solutions.

B. swap trip—a constraint which swaps two trips (which includes all tripresources and considers scheduling constraints, and costs). It movesthem back if the cost is no better.

C. mod_gap—a constraint which utilizes the concept of a slack schedulingpercent to try to add gaps between resource utilization to minimizeconflicts. These gaps may only be at places where sidings are found,thus providing an abstract siding capability. It tries to minimize thenumber of gaps introduced.

D. change_equipment—a constraint which assigns a different train type tothis trip when trains are over constrained.

E. move_group—a constraint which moves a group of trips to takeadvantage of the time available for scheduling. Without it, a tightscheduled would have gaps of time between groups of trains which are notutilized.

In one embodiment of the present invention, the scheduling systemutilizes a cost reactive resource scheduler to minimize resourceexception while at the same time minimizing the global costs associatedwith the solution. For a given set of orders, resource exception is theamount of time that two or more resources are in conflict, e.g., theduration of time that two trains are scheduled to be using the sametrack at the same time. These two goals of minimizing resource exceptionand minimizing global costs are inversely related, where reducingresource exception typically means increasing global cost. This is dueto the fact that a low resource exception solution can always be foundby delaying the start or arrival of trains sufficiently. This means lesstraffic can flow over time, however, and thus more cost is incurred insuch a schedule. A cost reactive scheduler may be used to develop aschedule by evaluating the resource exception and the cost associatedwith moves resulting in a schedule which is not only resolvable, butrepresents a more minimal cost solution. Since the movement planner isdesigned as a hierarchical system, and the abstraction used forresources and movement by the scheduler leave room for the movementplanner to resolve minor conflicts, the resource scheduler does not haveto remove all resource exceptions for a successful solution to be found.

After experimentation with many different orders for train resources, awide variety of track architectures, and differing costs functionsassociated with the orders, it has been determined that a scheduler thatcan provide a schedule where the total resource exception time was nomore than approximately 1% of the total unopposed trip time for allorders results in a resolvable schedule at the lowest global cost.Accordingly the cost reactive scheduler comes as close to this solution,i.e., target total resource exception time of 1% of the total unopposedtrip time, as possible, without going under it, to minimize theresultant global cost. The cost reactive scheduler is able to achievethis minimum global cost by evaluating the “goodness” of each move interms of resource exception and cost associated with each move.

As discussed in more detail below, the cost reactive scheduler does notresolve each scheduling problem in the same way. The cost reactivescheduler initially classifies the set of orders for train resources andthen generates a schedule using scaling parameters and acceptancecriteria which are dependent upon the classification of the schedulingproblem. For example, the cost reactive scheduler may initially classifya set of order for train resources into one of four categories:

Cost Constrained and Resource Unconstrained

Cost Constrained

Normal

Resource Constrained

If it appears that there is plenty of slack in the solution space suchthat achieving the 1% target resource exception will not be a problem,the scheduler will classify the problem as “Cost Constrained” and willemphasize cost. If it appears that there is excessive slack in thesolution space, the scheduler will classify the problem as “CostConstrained and Resource Unconstrained” and will emphasize reducing costeven more. If the scheduling problem appears to have insufficient slackto achieve the 1% target resource exception, the scheduler classifiesthe problem as “Resource Constrained” and will emphasize reducingresource exception at the expense of cost. All other scheduling problemsmay be considered ordinary and have a straight forward solution and maybe classified as “Normal”.

Based on this classification of the scheduling problem, the costreactive scheduler will determine a scaling parameter which may beapplied to the resource exception or the cost associated with each moveto emphasize either the resource exception or the cost as a function ofthe classification of the scheduling problem. Throughout the searchphase, the cost reactive scheduler searches for moves that approach thetarget resource exception of 1%. Unlike previous schedulers which usedfocused simulated annealing to search for scheduling solutions having 0%resource exceptions, the cost reactive scheduler accepts a lessersolution, in order to preserve moves for later in the search phase thatwould not otherwise be available if the 0% resource exception solutionwas initially accepted. Because the reactive scheduler is not searchingfor the “perfect” or 0% resource exception, the scheduler may accept amove where the result of the move results in an increase of the cost orresource exception. However, a schedule with minimal global cost willresult from searching for moves that approach the target resourceexception of approximately 1% of total unopposed trip time for the setof orders for train resources.

With reference now to FIG. 12, a graphical representation of the searchphase of the cost reactive scheduler is shown as a plot of resourceexception versus temperature steps—where temperature steps represent theend of several move operations in the search. FIG. 12 shows a plot ofthe ideal trajectory of the 1% target resource exception as a dashedline versus the solution generated by the cost reactive scheduler as asolid line. In this plot resource exception is in units of time(seconds) and more positive resource exception means an improvement.

With continued reference to FIG. 12, a scaling parameter was used tonormalize and weight a change in resource exception in a given searchoperation so that a change in cost could be compared directly withresource exception to determine whether the move is good or not.Depending upon the scaling parameter used by the scheduler, thetrajectory can be shifted up or down the resource exception axisresulting in a higher or lower final resource exception value. In thecurrent embodiment of the scheduler, the initial scaling parameters arechosen in an effort to achieve the 1% target resource exception. Thescaling parameter may comprise two components, a normalizing componentand a biasing component. The normalizing component is determined duringa first phase of the search. The biasing component is determined aftereach move and forces the resource exception towards the 1% trajectory.

With continued reference to FIG. 12, the ideal trajectory wasrepresented by a polynomial derived from a successful run of a prior artscheduler that does not contain the cost reactive modifications of thepresent invention. The solution of the cost reactive scheduler (solidline) can be seen varying above and below the trajectory during thesearch. The effect of the cost reactive modifications is to pull theactual values toward the trajectory.

With reference now to FIG. 13, a simplified piece-wise linearapproximation of the idealized trajectory was found to accomplish thedesired goal in a more efficient manner than by deriving a polynomial torepresent the experimentally determined trajectory. The approximation isshown as a dashed line in FIG. 13. The target trajectory is representedby cost emphasis linear phase with zero slope (and an exception value of−30000 seconds in this example), followed by one with a slope like theidealized curve. The maximum number of temperature steps representingthe cost emphasis phase is a dependent upon the classification of thescheduling problem.

It is important to emphasize cost during the initial steps of the searchphase because it is rather easy to reduce resource exception by largeamounts during the first steps of the search which may result in aresolvable solution which does not minimize global costs. This reductionin resource value is seen in FIG. 12 as a steep linear slope for thefirst few hundred steps followed by a knee in the curve and then ashallower linear slope to the end of the search. The emphasis on costearly in the search generally serves two purposes. First, if thescheduler reduces resource exception too quickly, it will pack thetrains too closely and not be able to meet the 1% target. Second, theresulting global cost of the schedule would be much larger. Therefore,the cost reactive scheduler emphasizes cost early on in the search. Thetime spent emphasizing cost depends upon the classification of thescheduling problem as discussed more fully below.

A similar plot for cost versus temperature steps (not shown) would showthat cost increases as the resource exception decreases. It is thenature of this directed conflict between cost and resource exceptionthat alternatively permits uphill moves resulting in a worse solution ineither resource exception or cost but which enables the scheduler toarrive at a more globally optimized solution in both resource exceptionand cost.

It has been determined, over many months of testing, that a coarseschedule from the cost reactive scheduler which results in a resourceexception time which is approximately 1% of the total trip time, willresult in a resolvable schedule by the movement planner. Accordingly,the goal of the cost reactive scheduler is to generate a schedule withinthis target range—thus insuring a low cost solution.

In operation, the cost reactive scheduler may receive a set of ordersfor train resources (the movement of trains or utilization of otherresources) which define a scheduling problem. Each order will have acost function, typically a polynomial equation associated with it. Theorder may also have a scheduling window associated with it i.e.,earliest departure time and latest arrival time.

Initially, the cost reactive scheduler will classify the schedulingproblem as a function of the slack associated with the orders. Slack isthe accumulation for all of the orders of the differences for each tripbetween maximum trip time based on the scheduling window and the minimumtrip time based on maximum throttle. The scheduler may compare the slackassociated with the scheduling problem with the total trip time for thescheduling problem, and classify the scheduling problem as CostConstrained, Cost Constrained and Resource Unconstrained, ResourceConstrained or Normal as previously discussed.

For example, if the problem slack time is greater than a predeterminedparameter, which may be defined in the COPES database, then thescheduler should be able to achieve the target 1% resource exception andthe problem may be classified as “Cost Constrained”. If the slack timeassociated with the scheduling problem is greater that 150% of the totalunopposed trip time then the problem may be classified as “CostConstrained And Resource Unconstrained.”

If the problem is not Cost Constrained, then another predeterminedparameter may be used to determine if a problem is Resource Constrained.For example, if the resource exception time divided by the total triptime is greater than this predetermined parameter, then the resourcescheduler may classify the problem as “Resource Constrained”.

If it is determined that the scheduling problem does not meet thecriteria of the above three classifications, then the scheduler mayclassify the problem as “Normal”.

The scheduler may perform several other functions before initiating thesearch phase. As explained above, the scheduler will emphasize costsduring the beginning of the search phase (“cost emphasis phase”). Theduration of the cost emphasis phase may be based on reducing theresource exception to a specified level, or on a maximum number of movesor temperature steps. For example, the scheduler may determine theinitial resource exception value and the initial cost associated withthe scheduling problem. The scheduler may then determine the level towhich the resource exception value must be reduced in order to stopemphasizing costs. The scheduler may also determine a maximum number oftemperature steps for emphasizing costs based on the classification ofthe problem, with the Cost Constrained problem requiring the mosttemperature steps and Resource Constrained problem requiring the leasttemperature steps.

Initially, the scheduler may estimate target resource exception as aspecified percentage of the total trip time by adding a percent to thetotal minimum trip time and dividing by 100. Once the search is begun,the target resource exception may be updated periodically, e.g., afterevery 800 temperature steps.

The initial scaling parameter may be defined by the COPES database. Oncethe search phase begins, the scaling parameter may be updatedperiodically. For example, the search phase may comprise a first phaseand a second phase. In the first phase, the normalizing component of thescaling parameter may be determined and updated after every move. Thenormalizing component may be defined as the ratio of the change inresource exception versus the change in cost. The scheduler may alsodefine the normalizing component as a function of the classification ofthe scheduling problem. For example, if the scheduling problem isResource Constrained, then the scheduler may retain the largest changeratio as the scaling parameter. For all other classifications, thescheduler may retain the ratio of the average change in resourceexception versus the average change in cost as the normalizingcomponent. The duration of the first phase of the search may be definedby the COPES database, e.g., the initial 100 temperature steps. Afterthe first phase, the normalizing component is no longer updated andremains constant throughout the second phase of the search.

Throughout both the first and second search phase, the effect of thenormalizing component is modified and updated after each move by adynamically changing bias. The biasing component may be used to forcethe resource exception toward the target trajectory.

The first phase begins the search for a solution to the schedulingproblem by making a random move. The resulting resource exception forthe problem and the cost associated with the move may be determined byapplying the initial scaling factor (for the first move) to the resourceexception value and the cost as a function of the classification of thescheduling problem. For example, if the problem is Cost Constrained, thecost will be weighted more heavily than the resource exception.

In each subsequent move, the normalizing component from the previousmove is used to determine the scaling parameter for the subsequent move.The biasing component may continue to be determined during thesubsequent move as a function of the resource exception as compared tothe target resource exception. The normalizing component may be updatedduring each move as a function of the ratio of the change in resourceexception versus the change in cost.

The determination of whether a move is accepted is a function of theclassification of the problem, and the change in the resource and costassociated with the move. For example:

a) If the change in resource exception and the change in cost are bothimprovements over the previous move, then the move is accepted for allclassifications;

b) If the change in resource exception and the change in cost are bothworse, then reject the move for all classifications;

c) If the if the change in resource exception is worse, but the changein cost is an improvement, accept the move if the magnitude of thechange in cost is greater than the magnitude of the change in resourceexception; and

d) If the change in resource exception is an improvement and the changein cost is worse, accept the move if the magnitude of the change inresource exception is greater than the magnitude of change in the cost,unless

-   -   1. the search is not in the Cost Emphasis phase and the resource        exception is already better than the target trajectory; or    -   2. the scheduling problem is Cost Constrained and the resource        exception is already better than the target trajectory.

The scheduler may logarithmically reduce the effect of cost change asthe search progresses by de-emphasizing uphill resource exception movesas the search temperature decrease, e.g., by including a log10 factorbased on the number of temperature steps.

The second phase of the search is similar to the first phase with theexception that the normalizing component of the scaling factor remainsconstant. Therefore, the scaling parameter is only adjusted as thebiasing component moves the resource exception closer to the targetresource exception.

From the foregoing, it will be apparent that the resource scheduler 330globally optimizes scheduling of the trains by abstracting both trainmovement and resources. The use of the focused simulated annealing inCOPES focuses attention on the critical areas. The generation of moveoperators, although random, is more directed by allowing the constraintsattached to each trip to make decisions regarding the usefulness ofmodifications to the global solution. The use of a cost reactivescheduler may be used to develop a schedule by evaluating resourceexception and the cost associated with the moves to result in a schedulewhich is not only resolvable, but represents a minimal cost solution.

Movement Planner.

As shown in the system block diagram of FIG. 3, the order scheduler 200provides the schedule information to the planner/dispatcher 204, aportion of which i.e., the movement planner 202, is illustrated ingreater detail in FIG. 6.

With reference now to FIG. 6, the movement planner comprises a movementplanner initializer 400, a movement planner executor 402, a physicalmodel 404 (preferably a stand alone unit as illustrated in FIG. 8), adisplay, a resolution options identifier 408 and a conflict resolver410.

The movement planner initializer 400 receives the schedule from theorder scheduler 200 of FIG. 3 through the planner/dispatcher 204. Themovement planner initializer 400 also receives information regarding thestate of the system from any suitable conventional external source,generally from the dispatching function of the planner/dispatcher 204.This information may be developed from a variety of sources such as thegeolocating system (illustrated in FIG. 10) or conventional tracksensors for determining the location of trains in the system.

The schedule and the data as to the state of the railway system are usedalong with the definition of each of the trains and their starting pointto initialize the movement planner. The definition of a train mayinclude all relevant data such as the number and type of locomotives,the number and type of cars and the weight of each of the cars. Thestarting point of each train includes its position of the train in thesystem, its direction on the track, and its velocity. As a minimum foreach of the trains, the schedule includes: the originating point, a timeof departure from the originating point and a destination point. Thisdata is a “state vector” which is supplied to the movement plannerexecutive 402 along with a time interval which indicates the extent oftime that the movement planner 202 should plan train movements.

The movement planner initializer 400 may be any appropriately programmedgeneral purpose or special purpose computer.

The movement planner executor 402 receives the schedule and state of thesystems data from the movement planner initializer 400 and is connectedfor two-way communications with the physical model 404 and theresolution options identifier 408. The movement planner executor 402also receives information from the conflict resolver 410 and providesinformation to the planning/dispatching function through a terminal 406.

The movement planner executor may be any appropriately programmedgeneral purpose or special purpose computer. The movement plannerexecutor 402 receives and records the state vector, and uses theservices of the physical model 404 to advance time in increments until(a) the physical model 404 reports a train conflict, (b) a specific stopcondition occurs or (c) the simulation time interval is reached.

If a train conflict is reported by the physical model 404, the statevector at the time of the conflict is saved and the conflict is reportedby the movement planner executive 402 along with the data reporting thetime history of the motion of the trains. Alternatively or in addition,the existence of and background information relating to the detectedconflict is reported by the physical model 404 to the conflict resolver410.

The physical model 404 follows the motion of the train once it has beenprovided by the movement planner executive 402 with data identifying theinitial state, stopping condition and the time advanced interval.

The resolution options identifier 408 receives the notice of a conflictfrom the movement planner executor 402 and identifies the optionsavailable for the resolution thereof.

The conflict resolver 410 receives the identified options from theresolution options identifier 410 and performs an analysis based on theperformance measure data received from terminal 332 of the orderscheduler 200, FIG. 4. This evaluation is accomplished by simulatingeach of the options and computing the associated performance measure orfigure of merit.

If “local optimization” is desired, this “best” result is reported tothe movement planner executor 402 for display to the dispatcher and/orthe movement plan is revised to include the alternate path, ifapplicable, and the simulation using the physical model 404 is repeatedbeginning from the initial state or other recorded state. Localoptimization is satisfactory in a large percentage of scenarios if theschedule provided by the order scheduler 200 of FIG. 3 has beensufficiently intelligent in specifying the dispatching times. If thedispatch times are not carefully specified, local optimization may leadto “lockup”, i.e., a condition in which conflicts may no longer beresolved. Lockup occurs because the resolution of one conflict leads toor limits the alternatives for resolution of another set of conflicts.

“Global optimization” may be performed using a variety of optimizationtechniques, preferably a version of the well known “branch and bound”technique for searching a tree of alternative solutions. In the branchand bound technique, each of the conflicts is modeled as a branch pointon a decision tree. As the simulation proceeds and conflicts areresolved, the search technique chooses the lowest cost alternative andcontinues the simulation. The cost of alternatives is saved, as is thestate of the system for each of the conflict points. It is possible thatchoosing the lowest cost solution among the alternatives may not resultin the optimum overall solution. The branch and bound technique allowsthe search to back up in the tree and retract decisions previously madein order to reach a lower cost solution or avoid a lockup.

The movement plan available at the dispatcher terminal 406 desirablyincludes a suitable conventional display to display the motion of thetrains until a conflict occurs, and to present the time history leadingup to the conflict in a graphical form for interpretation and resolutionby a human operator.

In addition, the data from the optional resolution options identifier408 may be displayed to the operator to assist him in manually resolvingthe conflict. In addition to the options and the cost associated witheach, the conflict resolver 410 may provide a suggestion as toresolution of the conflict and that suggestion may also be displayed tothe operator.

The Scheduling Process

The interaction of rule based and constraint based systems in the orderscheduler of FIG. 4 and the movement planner of FIG. 6 may be morereadily understood by reference to the system as illustrated in FIG. 8.

As is well known, a constraint is a limit on the value of an entity.Constraints considered in this description generally fall into threecategories, those time constraints which are inherent in the task offilling an order, those constraints which are inherent in the structureof the railroad, and those constraints which are explicitly specified bythe user.

Order constraints include the sequential nature of the activities basedupon the fact that a train cannot jump from one point to another withoutpassing through some intermediate segments. For example, in order toload coal at a mine, a train must capture the track segments, in theappropriate order, from the place at which the train originates to thedestination mine and only then capture the track segment at the mine andthe mine loading equipment.

Constraints are also inherent in the structure of the railroad. Suchconstraints include gap-able elements (sidings located between segments)and single/multiple track configurations. A wide variety of user definedconstraints may be included. These constraints are generally timeconstraints which seek to restrict the resource scheduler 330 fromscheduling certain resources over certain time periods.

One example of such a constraint is a mine which has limited hours (e.g.daylight only) during which it can load coal. Such a constraint would beincluded by limiting the resource availability to a specified interval.Another example of such a constraint is resources, such as track orlocomotives, which are out of service for maintenance during a specifiedtime interval. Still another example is a train which is not under thecontrol of the scheduler, e.g., a passenger train which is scheduled byan entity external to the freight train scheduler. All of theseconstraints may be included by appropriately defining the resourceavailability timelines.

The rule-based process converts orders into a form which is suited to aconstraint-propagation solution and restricts the search space byeliminating certain candidate solutions, based upon a set of rulesincorporating company policy, standard operating procedures andexperience factors, among others. The constraint-based process solvesthe problem of moving time intervals to maximize the externally suppliedperformance measure while satisfying all of the constraints. The resultof this process is a schedule for railway operation which includes aglobally optimized schedule for train operation, maintenance activities,and terminal equipment.

As shown in FIG. 8, each of the processes may be implemented as anasynchronous UNIX process with inter-process communications between thetwo processes implemented using a well known client server relationshipbased upon UNIX sockets.

In the event that the procedural means is provided, it also isimplemented as one or more asynchronous UNIX processes. These processescommunicate using a well-known client-server inter-processcommunications. The procedural means is used to refine the schedule toinclude details of the rail system. This is accomplished by simulatingthe operation of the railroad, identifying the conflicts in the schedulewhich result from the level of model abstraction used in theconstraint-based process, and adjusting the schedule to eliminate thoseconflicts while at the same time maximizing the performance measure.

Once this is achieved, the movement plan obtained by refining theschedule is returned to the rule-based processor. If for any reason, allconflicts cannot be resolved, the movement plan is returned to therule-based processor with the conflict duly noted. The rule-basedprocessor examines the movement plan based upon set of rules depictingcompany policies and, if the movement plan is satisfactory, forwards themovement plan to the dispatcher for display or for use in controllingthe applicable trains as described infra.

Operation of the system of the present invention may be seen withcontinued reference to FIG. 8, in which orders, the identification ofextrinsic traffic, schedule exceptions, and an identification theresources available as a function of time to accomplish the order arereceived by a user interface 500. A schedule exception is a predictedfailure to meet a defined schedule which requires rescheduling of theinvolved resource and possibly other affected resources. Extrinsictraffic is pre-scheduled traffic not to be altered by the system. Ordersmay arrive as a batch or arrive in a sequence over a period of time.

The user interface 500 translates this data into “facts” and assertsthem into the rule-based process. The user may also add, remove, orchange certain rules in the rules database for the purpose of includingcompany policy and other experience factors which may change over time.

The user interface 500 provides data to a rule based expert system 502.A variety of expert system tools are available to allow the facts to beasserted and processed by a rule-based inference engine according to therules contained in the rule data base. The preferred implementation isthe C-Language Integrated Production System (CLIPS) developed by NASAJohnson Space Flight Center because it is readily imbedded into a systemand supports an object-oriented approach which is compatible with theconstraint-based element.

The functions of this expert system are determined by a set of ruleswhich may be divided into several categories. Order specific rulesinclude rules which identify the sequence of activities with associatedresources which are required to fill an order and put the order into astructure which can be interpreted by the constraint based interferenceengine.

Order specific rules also include rules which determine the extent towhich scheduling will be performed in the event that a prior scheduleexists. For example, company policy may dictate that trips scheduled tobegin within a specified time period not be rescheduled upon receipt ofa new order, but may be rescheduled in the event of unforeseen delayswhich impact the existing schedule. These rules may be modified as newtypes of service, company policy, standard operating procedures, orexperience factors on the handling of orders are changed.

A second category is rules which receive availability information fromthe user interface 500 and process these rules into a form which issuitable for application to the constraint based process. Availabilityis modified to account for extrinsic traffic, locomotives out of servicefor repair or maintenance, track out of service, or other factors whichaffect the availability profiles.

A third category of rules are rules which restrict the search space forthe constraint based process. Rules are provided to determine the routeto be taken to accomplish the order. In many of the larger railroadsthere are multiple paths which can be taken to move a train from onepoint to another. This set of rules selects the optimum path based uponprincipals of physics, specified performance measures, standardoperating procedures or and experience factors. Trains which cannotservice an order because of locomotive power or terminal equipmentlimitations are excluded from consideration.

A fourth category of rules is those rules which evaluate the schedulereturned by the constraint based process and either resubmit the ordersto the constraint based process after relaxing some of the constraints,submit the schedule to the procedural means (if available), or notifythe user through the user interface 500 that the request is overlyconstrained and cannot be scheduled. If the procedural process isprovided, a fifth category of rules are those rules which evaluate theschedule and determine if it should be replanned, i.e. if there are noconflicts present, is it acceptable according to company policy and isit complete.

If implementation of the movement plan on the actual trains iscontemplated, then a sixth category of rules are those which receivenotification of deviations of the trains from the movement plan anddetermine whether or not re-scheduling should occur, and if therescheduling should be performed by adjusting the movement plan or theschedule.

A request to schedule an order from a scheduler client 504 may besubmitted via the client-server 508 to the constraint based expertsystem 510 for scheduling. Upon receipt of a schedule from the schedulerclient 504 which contains unresolved conflicts, the rule based expertsystem 502 determines the action to be taken. Depending upon the rules,this action may include rescheduling or, if the unresolved conflict issmall, the schedule may be forwarded to the procedural means (ifavailable) to resolve in the course of refining the schedule into thedetailed movement plan.

The schedule may be passed to a dispatcher terminal/display 506 ifdesired for display to an operator (e.g. a dispatcher) or to automateddispatching. If the procedural process 516 is available, the schedulealong with a performance measure may be passed there via the movementplanner client 508 for refinement.

The scheduler client 504 may receive a schedule request from therule-based expert system 502, translate it into a structure understoodby the scheduler server 508 and submit it to the scheduler server 508.This schedule request may includes one or more orders. As earlierdescribed, an order may contain information such as the total quantityof commodity (if the order is for bulk delivery), the earliest time thatpickup can occur, the latest time for delivery, and a performancemeasure reflecting penalties for late delivery and/or incentives forearly delivery. In addition, the order may reflect the activitiesrequired to service the order and the resource types (e.g., trains)suitable for servicing the order.

Further, an order may include a percent of full speed parameter and aslack time percent parameter. The percent of full speed parameterindicates that the schedule should be built with the trains running atless than maximum speed, thus giving the movement planner more latitudein satisfying the resulting schedule. The slack time percent provides alimited amount of cushion within which the movement planner can move thetrain trips to assure meeting the overall schedule.

In the reverse direction, the schedule client 504 receives the schedulefrom the constraint based system 510 via the scheduler server 508, andtranslate it into a fact which can be asserted in the rule-based expertsystem 502.

The schedule server 508 receives an order in the form described aboveand translate it into a form compatible with the constraint based expertsystem 510. It also translates the schedule produced by the constraintbased expert system 510 into a form compatible with the scheduler client504. The scheduler server 508 and the scheduler client 504 communicateusing client-server inter-process communications well known in the art.

The constraint propagation expert system 510 satisfies a set ofconstraints describing an order asserted by the scheduler server 508.All of these constraints may be included by appropriately defining theresource availability timelines.

Constraints specified by the user include resources, such as track orlocomotives, which are out of service for maintenance and train notunder the purview of the scheduler, such as an Amtrack train which isscheduled by an external entity.

The preferred implementation for the constraint based system 510 is thewell known search technique known as simulated annealing. However, othersearch techniques such as genetic search may be suitable for someapplications.

Simulated annealing may be implemented using a constraint propagationshell based upon the Waltz algorithm (described, e.g., in “UnderstandingLine Drawings of Scenes with Shadows,” The Psychology of ComputerVision, ed. P. Winston, McGraw-Hill, New York, 1975).

The capability to translate the sequence of activities in the activitylist to a sequence of time intervals may be provided by a commerciallyavailable train performance calculator.

Alternatively, a custom developed process based upon the Davis Equationsfor train motion or suitable conventional means may be used to ofestimate the time required for a resource to complete a specifiedactivity. If alternative resources are available for accomplishing anactivity, then alternative intervals are defined for each activity. Alist of intervals, grouped by resource and by time may thus be produced.

Intervals are grouped together in a logical way, typically initially onthe basis of entire train trips (if applicable to a particular order).

Planning is performed initially with the groups and then is divided intogap-able intervals for continuing the search process. A gap-ableinterval is an interval in a group after which a gap is allowed beforethe next interval in the group. This representation is used to representthe presence of a siding or other capability for holding a train for aninterval of time while another train passes. Capability is provided toreceive the interval groups, resources available intervals, andperformance measures and conduct a search for a schedule which (a)satisfies the resource availability constraints, (b) satisfies theinterval constraints, and (c) minimizes the performance measures.

When the search algorithm has completed its search without finding asolution, the interval groups are further subdivided or gapped, theintervals regrouped and then the search is continued using the smallertime intervals. Upon completion of the search with the smallestintervals, the resulting movement plan is forwarded to the schedulerserver 508 for return to the rule-based system. If all of theconstraints cannot be satisfied, the movement plan is returned alongwith an indication that the schedule has conflicts and theidentification of the resources and activities involved in the conflict.

A display 506 is desirably provided to display the resulting movementplan for user examination. A variety of means may be used to display theplan. A popular approach is a standard stringline diagram used byrailroads. As illustrated in FIG. 2, the stringline is a line drawing inwhich the position on the track is plotted as a function of the time foreach train.

A movement planner client 512 is provided to translate the schedule intothe form of a request for planning which is compatible with the movementplanner server 514. Upon completion of the movement planning by theprocedural system 516, the movement plan is received from the movementplanner server 514 and translated into a form which is compatible withthe rule based expert system 502.

The movement planner server 514 translates the request for movementplanning into a form which is compatible with the procedural system 516.The server 514 also translates the movement plan received from theprocedural system 516 into a form which can be understood by themovement planner server 514. The movement planner client and movementplanner server 514 communicate using conventional inter-processcommunications.

The procedural system 516 receives the schedule and a state of the railnetwork (position of trains) from an external source and initializes asimulation capability with the definition of each of the trains andtheir initial point. The definition of a train includes the number andtype of locomotives, the number and type of cars and the weight of thecars. The position of each train includes its position of the train, itsdirection on the track, and its velocity. The motions of all of thescheduled trains is simulated until a train conflict occurs, a specifiedstop condition occurs, or the simulation time interval is reached.

If a train conflict occurs, the state vector at the time of the conflictis recorded and the options available to resolve the conflict aredetermined. If no conflict occurs, then the movement plan is completeand it is reported to the movement planner server 514 for forwarding tothe rule based system and for execution by the planning/dispatchingfunction.

The options available to resolve a conflict may be enumerated. Conflictsmay be classified as “meets”, “passes”, “merges”, or “crossings”. Theoptions for resolution of a conflict include moving one of the trains toan alternate track to await the passing of the conflicting train.Alternatively the departure of a train from its origin point or otherpoint at which it is stopped may be delayed until the way is clear.Still another option is to stop one of the trains at a point along itspath to allow the other train to move onto an alternate track. Theidentification of alternate track options and options for stopping alonga route are enumerated beginning with those options which are closest tothe point of conflict.

One of the advantages of the present system is evaluation of the optionsand the selection of the option which results in the best performancemeasurement. Best performance is determined by a performance measuresupplied by the rule based system. Evaluation of each option isaccomplished by simulating each of the options and computing theassociated performance measure. If “local optimization” is employed, themovement plan is revised to include the best alternative path (ifapplicable), and the simulation is rolled back to the closest point backfrom the point at which the trains involved in the conflict transferredto an alternate track. Local optimization is satisfactory in a largepercentage of scenarios because the prior scheduling operation performsa global optimization. Global optimization may be performed using avariety of optimization techniques.

It is desirable to use a version of the well known “branch and bound”technique for searching a tree of alternative solutions. In the branchand bound technique, each of the conflicts is modelled as a branch pointon a decision tree. As the simulation proceeds and conflicts areresolved, the search technique chooses the lowest cost alternative andcontinues the simulation. The cost of alternatives may be recorded, andthe state of the system may be recorded periodically. It is possiblethat choosing the lowest cost solution among the alternatives may notresult in the optimum overall solution. The branch and bound techniqueallows the search to back up in the tree and retract decisionspreviously made in order to reach a lower cost solution.

The Physical Model.

An important aspect of the present invention is the use of a physicalmodel of the topology of the railway system in several levels ofabstraction in the planning process. The topology of a railway systemmay be represented with multiple levels of complexity. This provides notonly the capability to model highly complex systems, but also to hidelevels of complexity where such complexity is a detriment to theefficient utilization of the model.

Preferably, and as shown in FIG. 9, an object-oriented rail topologymodel is composed of three fundamental elements, i.e., nodes, segments,and connectors. A segment is used to represent a length of rail whichmay be single or multiple track and is composed of an ordered collectionof fragments. A fragment is a piece of track which has constant grade,constant curvature, constant speed limit, and length.

A node may represent a complex object and may itself contain internalstructure composed of nodes, segments and connectors. Connectors areused at each end of a segment to join a segment to a node, and nodes maypossess an arbitrary number of connectors. Each element of the topologyis provided with a unique system identifier to enable the identificationof a location by reference to the system identifier.

At the highest level, a rail network is represented as a node. This railnetwork node contains structure which in turn can be represented as aset of nodes connected by segments. This first level of complexitymodels a rail network as a set of track segments connecting nodes whichrepresent gross entities such as ports, mines, setout yards, sidings,crossovers, forks, joins, and branch points. For simple track structuressuch as switches and junctions, this level of detail may represent themaximum level of detail. For more complex track structures such assetout yards, further levels of complexity may be added until the entirerail network is modelled in detail.

As illustrated in FIG. 9A, the node 900 at one end of a segment may be asiding 902 or a switch 904. The node 906 may represent an entire port,with multiple nodes.

As shown in FIG. 9B, the use of one or more nodes within a node isparticularly useful in developing different degrees of abstraction insomething as simple as sections of track.

The position of a train in a rail network is indicated by the positionof the head of the train. The head of the train is located by thesegment identifier and an offset from the connector on the segment. Inaddition, the direction of the train and the length of the train may beused to locate the remainder of the train.

With reference now to FIG. 7, data as to the position, direction andlength of a train may be used to calculate the resistance of the train,by taking into account the grade and curvature of the track fragmentsupon which the train is located, the train velocity and other trainparameters.

Routing from one point to another in the system may be computed by usingany network routing algorithm. The well known Shortest Path First (SPF)algorithm is frequently used. However, the algorithm need not usedistance as the performance measure in computing path length and morecomplex performance measures involving grades, for example, are oftenuseful.

The characteristics of the railroad rolling stock may be stored on aconventional resource database 800. This includes the physical andperformance data on each locomotive, its type, weight, length, crosssectional area, horsepower, number of axles, and streamline coefficients(both as lead and as following locomotive). For each car, the type, tareweight, length, cross sectional area, loaded weight, number of axles,and streamline coefficient may be provided. Unit trains are also definedin the database with an identifier, train speed limit, list oflocomotive types and list of car types. This resource database may beimplemented in tabular form, complex data structure, or using anycommercially available database.

The defined train objects may be propagated through the system inaccordance with requests for train movement provided by the simulationmanager support 802. All train movement is in accordance with theequations of physics, basic train handling principles, and well knowntrain control rules. The route of each train, provided by the simulationmanager support, may consist of an ordered list of fragments from thesource to the destination of each train trip with train direction oneach fragment also indicated.

The movement of the trains along the track is governed by simple physicsequations to compute the acceleration of the train. The initialacceleration of the train is bounded by the adhesion of the rails andthe weight of the locomotive. In addition the acceleration of some highhorsepower locomotives may be limited by the force that would cause thetrain to uncouple.

It is desirable that the train handling rules allow the train toaccelerate with maximum acceleration subject to the available tractiveforce of the locomotives, maximum tractive force at the rails, and thedecoupling force. These values are typically set somewhat lower thanactual to allow for conservative handling of the train by an engineer.Once the scheduled speed, or speed limit (if is lower) is attained, thetractive force of the train is set exactly equal to the resistance ofthe train in order to maintain the speed.

Train braking is applied to stop the train, to reduce speed to a lowerspeed limit, to avoid interfering with another train or in response to asignal, and to maintain a safe speed on a grade. Many techniques areavailable to model train braking. The capability to anticipate brakingneeds is provided by searching the track ahead for speed limit changes,other trains or signals.

Three means are provided for controlling a train in order to move pluraltrains in the network without conflict. These control methods are “nocontrol”, “moving block control”, and “fixed block control”. The nocontrol method is used to move a single train through the network. Thetrain moves through the network with no concern for the signaling systemor the presence of other trains. This method is useful when computingdata on the unopposed run time for a specific train over a segment oftrack for use in producing the schedule.

In the fixed block control method, a train checks the railway signallingmodel at each time interval to determine if a signal is visible to thetrain and if so, whether the signal indicates that the train shouldcontinue, slow or stop. Specific rules in the signalling system dependupon the railroad which is being modelled. The control behaviorindicated by the railway signalling model supersedes all other speedlimits.

Moving block control is based on establishing a forbidden zoneassociated with each train. The forbidden zone for a train includes thetrain and a length of track in front of and along the route of the trainwhich is equal in length to the stopping distance of the train plus anyambiguity as to the train's position. The stopping distance is of coursedependent upon the speed of the train, the grade, the track adhesioncoefficient, and the weight of the train. This requires that each trainmonitor the position of the forbidden zone of other trains to assurethat the forbidden zone of no other train enter its forbidden zone. Toavoid such an incident, brake handling rules are applied to assure thatthe train decelerates in an appropriate fashion to avoid conflict.

As the trains are advanced incrementally in time, the positions of thetrains relative to the specified stop conditions are monitored. If astop condition occurs, the time advance ceases and the results includinga time history of the path of the trains is reported.

In the event that conflicts occur between trains (such as the contact oftwo forbidden zones, the time advance ceases and the results, along withthe type conflict, trains involved, and location are returned to thesimulation manager support 802 to support the resolution of conflicts byan external process.

A signalling system based upon conventional fixed block signals may bemodeled. Signal blocks are defined and related to the fragment trackstructures used in the multi-level modelling of rail topology. As thehead of a train occupies a fragment associated with a signalling block,the status of the block changes from “unoccupied” to “occupied”. Whenthe tail of a train exits all fragments within a block, the block statusis changed to “unoccupied”. The relationship of the block status to thesignals is defined by a set of company-specific railway rules which arepart of a standardized set. Information on these rules may be obtainedfrom publications of the American Association of Railroads and othersources. The automatic block signalling (ABS) is well known and may beused as an illustrative implementation.

There are two classes of responses which occur when a train enters orexits a signal block, i.e., control of following trains and control ofopposing trains. In the case of following trains, and assuming a typicalfour level signalling system, the signal at the point of entry of theblock becomes a “stop” for trains following the subject train. Rule 292applies which requires a stop for a following train. This signalcondition continues until the tail of the subject train has exited theblock. At this time the signal is set to “restricted speed”,corresponding to Rule 285 which requires a following train to proceed ata restricted speed and prepare to stop at the next block. As the subjecttrain exits the next block in advance, the signal is changed to yellowover green, corresponding to Rule 282 which requires a train to approachthe next signal at restricted speed. When the train finally exits thethird block in front of the signal, the signal changes to “clear”, andcorresponding to Rule 281 which allows the train to proceed inaccordance with all applicable speed limits.

In many systems the four level signaling system is implemented by twovertically spaced lights, i.e., red over red is stop, yellow over red isrestricted speed, yellow over green is medium speed, and green overgreen is clear.

The signals may also be set for opposing trains. These signals must beset in accordance with the track topology to assure that opposing trainsdo not enter a track segment with no alternate track when an opposingtrain is in the same track block. The extent to which a train entering ablock causes opposing signals to be set is defined in the signalingsystem for each signal block.

The condition of the signals may be passed to the train movement meansupon request and is updated each simulation interval based upon theposition of the trains as reported by the train movement.

A simulation support manager is provided to initialize the resourcedatabase, the multi-level modelling of rail topology, the railwaysignalling model, and the train movement in response to an externalrequest to perform a simulation. The request to perform a simulationincludes the simulation time, the schedule, route, time, increment,trains and their locations, and a list of scheduled actions. Anexternally supplied schedule contains a route for each train and aschedule for each train. The schedule specifies the list of fragmentsover which the train will pass and the time that a train departs from astopped point on the route.

Scheduled actions include “move train to fragment x and stop”. Acapability is thus provided to move the trains forward, by issuing acommand to the train movement section 804 of FIG. 8, until the nextevent and to report back to the requesting external processor. The nextevent may be a scheduled event, or it may be an unscheduled event suchas a train conflict. Upon completion, whether caused by reaching ascheduled event or caused by an unscheduled event, the history of thesimulation and the stop condition or conflict situation encountered isreturned to the external process which requested the simulation.

Train Control.

With reference to FIG. 10, at least of the locomotives driving each ofthe trains in the system of the present invention is configured to havea train controller 208. The train controller 208 receives as much of themovement plan as is applicable to it. As explained further below, thetrain controller 208 desirably contains a train pacing system whichutilizes the track data model, the train handling constraints and actualtrain position and velocity data, wind data and track condition data tocompute a set of train commands which, if implemented, will cause thetrain to operate on the trajectory provided in the movement plan. Thecommands determined by the train pacing system may be displayed on adisplay 220 in the cab of the locomotive for execution by the driver or,alternatively, would be suitable for direct semi-automatic control ofthe train through convention activations 222, i.e., the commands coulddirectly control power settings and braking (with an driver override, ifdesired).

To evaluate its progress against the trajectory of the movement plan,the train controller 208 may be equipped with a satellite based positiondeterminer, such as the Global Positioning System (“GPS”) 226 and mayreceive signals from a portion of the track transducer system discussedabove. Use of the satellite based position determining system wouldeliminate the need for most of the transducers, except those at controlpoints, providing considerable reduction of the costs of railwaymaintenance.

In the system of the present invention, transducers are needed only atthe control points, such as switches, in order to have positiveconfirmation as to which track of many nearby parallel tracks, aparticular train is on (or that a train has fully entered a siding). Thetransducers are used for these functions because they can be uniquelyidentified with a particular position on a particular track because thetypical earth satellite position determining system is accurate to onlyaround 35 feet. Since two parallel tracks could exist within that range,the trains occupancy of a specific track cannot be discriminated by theGPS. By using this combination of transducers only at control points anda earth satellite position determining system, the error of positiondetermining is capped at the accuracy of the satellite system (around 35feet) and is not dependent upon the near spacing of transducers.

Any other suitable position determining system may be used in thepresent invention, but the GPS and transducer system is particularlysuitable because of the low cost to install and maintain while providingsufficiently accurate position information.

As unforeseen conditions occur to the train as it moves along the trackin accordance with the movement plan, the train controller 208 canautomatically determine what new train commands are practical toimplement the movement plan safely. For example, if the engines are notproducing as much power as expected for their power setting, thecontroller can increases the power by issuing appropriate train commandsfor display or implementation as discussed above. In determining all thesettings, the train controller 208 takes into account a set ofapplicable safety rules and constraints, train handling constrains, andtrack parameters.

In situations in which the unplanned disturbances affect thecontroller's ability to keep the train on the movement plan trajectory,the train may return an exception notice to the dispatch portion of themovement planning function 202. Many times, the transmission of amessage of an anomalous condition by the train controller 208 will beentirely redundant as the dispatching function of the movement planningfunction 202 monitors the state of the system, particularly against themovement plan, and may be already attempting to replan the movement planin light of the new information regarding the system state, i.e., theanomaly which has occurred to one or more trains.

With reference to FIG. 11, the train controller 208 may be understoodwith reference to the functions which may be carried out to provide thedesired control of each train. Specifically, the train controller 208aboard each train controls the train in accordance with a movement planwhich is based upon a high fidelity model of a railroad.

A train movement plan is received from the movement planner, along withan initial power parameter (IPP) which was used in deriving the train'smovement plan. An initial power parameter of “1” means that the schedulewas prepared using full rated horsepower. In the present invention, theIPP is often made less than 1 in order to allow a train to make up sometime if it falls slightly behind the movement plan.

The movement plan may include a route (a list of fragments over whichthe train will pass) and the time of arrival for each control pointalong the route and the velocity of the train at that point. Inaddition, a train's movement plan may contain an identification of theareas in which speed will be restricted due to the anticipated presenceof other trains.

As explained further below, a train's movement plan may include data upto the next control point (e.g., a point at which a train must stop foranother train). As noted earlier, in addition to the movement plan andthe initial power parameter, the controller 208 may receive and/ormeasure data indicating the prevailing wind and track conditions, thecurrent position, the current time, the current velocity of the trainalong with the brake pipe pressure.

A predicted arrival time determine 230 may be provided to predict themovement of the train from its present position to the next controlpoint on the train's movement plan. A power parameter is initially setto the initial power parameter. External sources provide the currentstate of the train (current position of the train on the track and itsvelocity) and the current time. The route of the train with the powerparameter and the restricted fragments and the current state isforwarded to the Physical Model 232 to perform a simulation of themovement of the train over the track.

The physical model 232 returns the expected arrival time assuming thatthe train continues with the same power parameter. The physical model232 also returns a throttle and brake setting for the time intervaluntil the next update time. The throttle and brake setting is forwardedto the engineer's display means along with the expected time of arrivalat the next control point. Alternatively, the throttle and brake settingmay be used to control actuators which automatically make the throttleadjustment. The predicted arrival time and velocity at the destinationis passed to a power parameter adjuster 234.

The physical model 232 models the motion of a train over a detailedmodel of a track. The physical model 232 has the capability to representthe topology of a railway network with multiple levels of complexity. Inone embodiment an object-oriented rail topology model composed of threefundamental elements: nodes, segments, and connectors may be used. Asearlier explained, a segment is used to represent a length of rail whichmay be single or multiple track and is composed of an ordered collectionof fragments. A node may represent a complex object and contain internalstructure composed of nodes, segments and connectors, and nodes maypossess an arbitrary number of connectors. Each element of the topologyis provided with a unique system identifier to enable one to denote alocation by referencing the system identifier.

At the lowest level of detail, the physical model 232 represents a railnetwork as a node. This node contains structure which can be representedas a set of nodes connected by segments. This first level of complexitymodels a rail network as a set of track segments connecting nodes whichrepresent gross entities such as ports, mines, setout yards, sidings,crossovers, forks, joins, and branch points. For simple track structuressuch as switches and junctions, this level of detail may represent themaximum level of detail needed. For more complex track structures suchas setout yards, further levels of complexity may be added until theentire rail network is modelled in detail.

The position of a train is indicated by the position of the head of thetrain. The head of the train is located by the segment identifier and anoffset from the connector on the segment. In addition, the direction ofthe train and the length of the train may be used to locate theremainder of the train.

The physical model 232 also has the capacity to define a train objectand propagate it through the track network in accordance with requestsfor train movement provided by the predicted arrival time determiner 230or the power parameter adjuster 234. All train movement is in accordancewith the equations of physics, train handling practices, and traincontrol rules.

The train's movement along the track in the physical model 232 isgoverned by simple physics equations, based upon accepted train dynamicequations such as the Canadian National 1990 Equations, to compute theforces and hence the acceleration of the train. The initial accelerationof the train is bounded by the adhesion of the rails and the weight ofthe locomotive. In addition the acceleration of some high horsepowerlocomotives may be limited by the force that would cause the train touncouple.

In one embodiment of the present invention train handling rules allowthe train to accelerate with maximum acceleration subject to the powerparameter, available tractive force of the locomotives, maximum tractiveforce at the rails, and the decoupling force. Once the speed limit ofthe train or the track segment (whichever is lower) is determined, thetractive force of the train is set exactly equal to the resistance ofthe train in order to maintain the speed.

All motions of the train are kept in conformity with signals, which arereceived from external sources and act to slow or stop the train ifnecessary. For example, restricted speed fragments obtained with themovement plan are used to reduce the train's speed in the areas where itis anticipated that signalling effects will occur. If a moving blockcontrol scheme is being used, then external means may provide theposition of the immediate train in front and any other train which isscheduled to enter any fragment in the train's route.

The physical model 232 realistically models reduction in speed to alower speed limit, or in response to a signal, and to maintain a safespeed on a grade. Common commercially available or custom brake handlingalgorithms may be used to model train braking. Brake pipe pressure maybe provided by any suitable external means. The capability to anticipatebraking needs is provided by searching the track ahead for speed limitchanges, or may be provided by signals based upon precomputed brakingcurves. In one embodiment, a capability to determine the appropriatecombination of dynamic brakes, independent brakes and air brakes isprovided.

As the train advances incrementally in time, the position of the trainrelative to the specified stop condition (end of the route) is monitoredand, the time advance ceases when the stop condition occurs and theresults including a time history of the path of the train and itsthrottle settings are reported to the requesting means.

With continued reference to FIG. 11, the power parameter adjuster 234adjusts the power parameter to assure that the train arrives at thecontrol point “on-time”. The power parameter adjuster 234 may comparethe predicted arrival time and velocity with the detected time andvelocity and compare the deviation to a user specified allowabledeviation from the movement plan. If the difference between thepredicted arrival time and the scheduled arrival time exceeds theallowable deviation, the power parameter can be adjusted to correct. Todetermine the appropriate adjustment, several simulations of the systemmay be performed. In one embodiment, at least two or three values of thepower parameter are submitted sequentially to the physical model 232along with the route and the current state. Usually, the range of thevalues for the power parameter includes the value of 1 in order todetermine if the train's movement plan is now impossible. If the traincannot meet the schedule with a power parameter of 1, the train reportsa schedule exception to the dispatcher and offers a new predicted timeof arrival. In the event that a change in the power parameter willsatisfy the train's movement plan, a new power parameter is computed byinterpolation between the values that were simulated and is supplied tothe predicted arrival time determiner 230.

The suggested throttle setting, dynamic brake settings, independentbrake settings and air brake settings may be displayed on the cabdisplay 220 or to the driver.

ADVANTAGES AND SCOPE OF INVENTION

As is readily apparent, the system and method of the present inventionis advantageous in several aspects.

By the production of a detailed movement plan, tighter scheduling oftrains may be accomplished with a corresponding increase in thethroughput of the system.

By the use of a model of the physical system and the simulation of themovement of the actual train through the physical system rather thanstatistical averages, a movement plan may be produced which isrealizable by a train. When the statistical average of the time requiredfor movement of a train from node A to node B is used, the projectedposition of the train assumes instant acceleration and deceleration atall points in the route and a uniform average speed. This is true eventhough the effects of acceleration and deceleration were considered inderiving the statistical averages. Obviously, such a plan is notrealizable by a train and the deviation of the train from such projectedlocations cannot be used to modify train behavior. However, where thedetailed movement plan is actually realizable by the train, anydeviation therefrom can be used for control purposes.

Further advantages are obtained by the multilevel abstraction of thephysical model to meet the needs of the various components of thesystem. For example, statistical averages are sufficient in thegenerating of a course schedule and result in significant savings incomputer resources in a search for optimization, but are not sufficientin the optimization in the development of a detailed movement plan.

The combination of rule based and constrain based inference engines isparticularly advantageous. A rule based system is effective to narrowthe search for an optimum schedule, and provides the constraints for theconstraint based system to continue the investigation.

In the constraint system, the use of simulated annealing techniques toperform global searches for optimality provides a computationallyefficient means to reliability achieve a course solution. This solutionallows the fine grained investigation to be carried out by branch andbound techniques, thereby making optimization possible with the computerresources available.

Further, optimization is more quickly realized by conversion of allresource utilization to time intervals, and the use of search techniqueswhich group these time intervals in groups of varying sizes, with theentire trip first, and then breaking the groups down into increasinglysmaller groups only as necessary to obviate conflicts.

It is also a significant advantage for the operator of a railroad to beable to arbitrarily write rules relating to such things as businesspractices, labor contracts and company policy. For example, a companymay have a policy of delaying the departure of a train from a switchingyard for ten minutes if a specified number of additional cars can beexpected to be available within that time period. Such policy, whenwritten as a rule, becomes a constraint to the movement plan and wouldthus have been automatically considered in optimizing the movement plan.

Note that the optimization achieved by the present system is global,i.e. it includes both operating costs such as fuel and crews anddelivery costs such as premiums and penalties for the time of delivery.

By binding the detailed movement plan to the actual operation of thesystem, the time at which events occur can be relied upon in operatingthe system and conflicts in the use of system resources can be reducedto a shortened time period. Note that the effects of the binding of adetailed plan to a detailed operation are two-way: the fact that theoperation is closely controlled permits the schedule to be finely tunedand vice versa. By having both features, the present invention maysignificantly reduce the overall throughput on any operational system.

The system of the present invention permits conflicts to be resolvedwith respect to an overall optimization. Thus, for example, anoperational decision regarding the use of an asset, which was donelocally in the prior art, is done with reference to minimizing the costof the overall operation. In terms of the exemplary railway system, forfurther example, decisions regarding which of two trains should be sidedwhile the other is permitted to pass are made with respect to systemlevel impacts. Thus, the decision in which a train is not sided becauseit may save ten minutes locally but which ends up delaying downstreamtrains by many more minutes may be avoided.

Since train handling is included in the physical model, the use ofactual breaking curves for the specific train and track rather thanstatistical worst case scenarios will prevent much of the unnecessaryenforcement of safety stops common with the use of existing enforcementdevices. The use of simulation of the actual train will also reduce theseparation between trains required for safety and thus significantlyimprove throughput of the system.

While not necessary to the invention, the use of the present inventionin railway systems may reduce or eliminate the need for many of themaintenance-costly components in the railway control system. Forexample, in full implementation of the invention, a railway mayeliminate or substantially reduce the costly track signalling and aspectsystem. Many of the elements of the railway system which are local,including the personnel to operate such local components, may beeliminated or reduced.

While preferred embodiments of the present invention have beendescribed, it is to be understood that the embodiments described areillustrative only and the scope of the invention is to be defined solelyby the appended claims when accorded a full range of equivalence, manyvariations and modifications naturally occurring to those of skill inthe art from a perusal hereof. As is readily apparent, the system andmethod of the present invention is advantageous in several aspects.

1. In a multiple move, processor based simulated annealing method forresolving a scheduling problem associated with a plurality of orders fortrain resources, each order having a cost function and a schedulingwindow associated therewith, the improvement comprising the steps of:(a) establishing plural criteria for acceptance of a solution; (b)classifying the scheduling problem; and (c) selecting the criteria foracceptance of a solution as a function of the classification of thescheduling problem wherein the step of classifying includes the stepsof: (i) determining the total trip time associated with the plurality oforders; (ii) determining the total slack time associated with theplurality of orders; (iii) determining the classification of the problemas a function of the total trip time and the slack time wherein the stepof determining the classification is determined by the steps of: (a)selecting a predetermined percentage of total trip time to provide athreshold value; and (b) comparing slack time with the threshold value.2. The method of claim 1 wherein the selected percentage is less thanabout one hundred percent.
 3. The method of claim 1 wherein the selectedpercentage is more than about one hundred fifty percent.
 4. In amultiple move, processor based simulated annealing method for resolvinga scheduling problem associated with a plurality of orders for trainresources, each order having a cost function and a scheduling windowassociated therewith, the improvement comprising the steps of: (a)establishing plural criteria for acceptance of a solution; (b)classifying the scheduling problem; and (c) selecting the criteria foracceptance of a solution as a function of the classification of thescheduling problem wherein the step of classifying includes the stepsof: (i) determining the total trip time associated with the plurality oforders; (ii) determining the resource exception associated with theplurality of orders; (iii) determining the classification of the problemas a function of the total trip time and the resource exception whereinthe step of determining the classification is determined by the stepsof: (a) selecting a predetermined percentage of total trip time toprovide a threshold value; and (b) comparing resource exception with thethreshold value.
 5. A method for resolving a scheduling problemassociated with a plurality of orders for train resources by evaluatingavailable moves in a computer based simulated annealing process, eachmove resulting in a change in the resource exception associated with theproblem and a change in cost associated with the move, comprising thesteps of: (a) classifying the scheduling problem; (b) making a randommove; (c) weighting the resource exception and cost factors associatedwith the random move with a scaling parameter related to theclassification of the problem; (d) evaluating the resource exception andthe cost of the solution against a predetermined criteria; and (e)accepting or rejecting the move based on the evaluation wherein thepredetermined criteria is the classification of the problem.
 6. Themethod of claim 5 wherein the step of determining the scaling parameterby the steps of: (i) determining a normalizing component of the scalingparameter as a function of the change in resource exception and costfrom previous moves; (ii) determining a target resource exception as afunction of the number of previous moves; and (iii) determining abiasing component of the scaling parameter as a function of a comparisonof the resource exception of the current move to the target resourceexception.